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.     

Deep recurrent neural networks with word embeddings for Urdu named entity recognition

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state‐of‐the‐art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short‐term memory and back propagation through time approaches. The proposed models consider both language‐dependent features, such as part‐of‐speech tags, and language‐independent features, such as the “context windows” of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f‐measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.     

Design of a secondary standard for measuring arterial blood pressure

The measurement of blood pressure by mercury sphygmomanometer and auscultation is one of the oldest methods, and it is still widely used in health services. Nowadays, the usage of inexpensive electronic oscillometric devices for home and clinical use has increased dramatically. The purpose of this paper is to verify the reliability of such devices.     

ROS/TNF-α Crosstalk Triggers the Expression of IL-8 and MCP-1 in Human Monocytic THP-1 Cells via the NF-κB and ERK1/2 Mediated Signaling

IL-8/MCP-1 act as neutrophil/monocyte chemoattractants, respectively. Oxidative stress emerges as a key player in the pathophysiology of obesity. However, it remains unclear whether the TNF-α/oxidative stress interplay can trigger IL-8/MCP-1 expression and, if so, by which mechanism(s). IL-8/MCP-1 adipose expression was detected in lean, overweight, and obese individuals, 15 each, using immunohistochemistry. To detect the role of reactive oxygen species (ROS)/TNF-α synergy as a chemokine driver, THP-1 cells were stimulated with TNF-α, with/without H₂O₂ or hypoxia. Target gene expression was measured by qRT-PCR, proteins by flow cytometry/confocal microscopy, ROS by DCFH-DA assay, and signaling pathways by immunoblotting. IL-8/MCP-1 adipose expression was significantly higher in obese/overweight. Furthermore, IL-8/MCP-1 mRNA/protein was amplified in monocytic cells following stimulation with TNF-α in the presence of H₂O₂ or hypoxia (p ˂ 0.0001). Synergistic chemokine upregulation was related to the ROS levels, while pre-treatments with NAC suppressed this chemokine elevation (p ≤ 0.01). The ROS/TNF-α crosstalk involved upregulation of CHOP, ERN1, HIF1A, and NF-κB/ERK-1,2 mediated signaling. In conclusion, IL-8/MCP-1 adipose expression is elevated in obesity. Mechanistically, ROS/TNF-α crosstalk may drive expression of these chemokines in monocytic cells by inducing ER stress, HIF1A stabilization, and signaling via NF-κB/ERK-1,2. NAC had inhibitory effect on oxidative stress-driven IL-8/MCP-1 expression, which may have therapeutic significance regarding meta-inflammation.     

Frequent Failures of Motor Shaft in Seawater Desalination Plant: Some Case Studies

The failure of a shaft from a motor in a pump or a compressor has been a phenomenon of common occurrence in seawater desalination plants. The origin of the problem in majority of cases is either the inability of the material to withstand the level of dynamic stresses to which shaft is subjected during operation and/or inadequacy of the design. The shortcoming in the design may be responsible for initiating localized corrosion which ultimately leads to failure of the component. The mode of failure of the shaft could be stress-related failure such as stress corrosion cracking, mechanical fatigue or corrosion fatigue, and/or localized corrosion such as crevice corrosion. This paper describes some recent case studies related to shaft failures in seawater desalination plants. The case studies include shearing of a shaft in brine recycle pump in which a combination of environment, design, and stresses played important role in failure. In another case, ingress of chloride inside the key slot was the main cause of the problem. The failure in a high pressure seawater pump in a SWRO plant occurred due to cracking in the middle of the shaft.     

Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking can provide valuable information regarding public opinion towards government actions. With this motivation, this paper presents a new AI based SA tool for healthcare crisis management (AISA-HCM) in smart cities. The AISA-HCM technique aims to determine the emotions of the people during the healthcare crisis time, such as COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides, brain storm optimization (BSO) with deep belief network (DBN), called BSO-DBN model is employed for feature extraction. Moreover, beetle antenna search with extreme learning machine (BAS-ELM) method was utilized for classifying the sentiments as to various classes. The use of BSO and BAS algorithms helps to effectively modify the parameters involved in the DBN and ELM models respectively. The performance validation of the AISA-HCM technique takes place using Twitter data and the outcomes are examined with respect to various measures. The experimental outcomes highlighted the enhanced performance of the AISA-HCM technique over the recent state of art SA approaches with the maximum precision of 0.89, recall of 0.88, F-measure of 0.89, and accuracy of 0.94.     

Reaction of citric acid with calcite

The reaction of citric acid with calcite was investigated using the rotating disk apparatus. The effects of disk rotational speed, system pressure, and presence of magnesium and ferric ions on this reaction were examined. Scanning electron microscope (SEM) was also used to characterize the surface of the calcite disks at the end of each experiment.The reaction of citric acid and calcite is mass-transfer limited up to 500 rpm. The reaction rate of citric acid-calcite is limited by the precipitation of calcium citrate on the surface, especially at atmospheric pressure. Increasing the system pressure from 1000 to 1500 psi has no significant effect on the dissolution rate of calcite. The diffusion coefficient of 7.5 wt% citric acid in the presence of calcium citrate and calcium ions is at and 1000 psi. The presence of magnesium ions in citric acid solutions results in higher concentration of calcium ions in solution.Calcium citrate forms only on the disk surface, but not in the bulk solution. The morphology of the calcium citrate layer is dependent on the rotational speed of the calcite disk. More calcium citrate precipitation occurs at high disk rotational speeds, above 500 rpm, and at system pressures less than 100 psi. Calcium citrate appears to precipitate as feathery aggregates of radiating platy crystals on the surface of calcite.