Establishment of traceability for strain measuring data acquisition system in terms of voltage

This paper emphasizes on establishment of traceability for the strain measuring data acquisition system in terms of voltage. If this amplifier’s output voltage is not calibrated then traceability chain breaks. To complete the traceability chain, the amplifier’s output voltage has been calibrated for corresponding strain. The sensitivity is calculated using calibration results and further used to feed in data acquisition system to display the result in terms of force/strain.     

A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

The demand for cloud computing has increased manifold in the recent past. More specifically, on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs. The cloud service provider fulfills different user requirements using virtualization - where a single physical machine can host multiple Virtual Machines. Each virtual machine potentially represents a different user environment such as operating system, programming environment, and applications. However, these cloud services use a large amount of electrical energy and produce greenhouse gases. To reduce the electricity cost and greenhouse gases, energy efficient algorithms must be designed. One specific area where energy efficient algorithms are required is virtual machine consolidation. With virtual machine consolidation, the objective is to utilize the minimum possible number of hosts to accommodate the required virtual machines, keeping in mind the service level agreement requirements. This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host. The online algorithm is analyzed using a competitive analysis approach. In addition, an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms. Our proposed online algorithm consumed 25% less energy and performed 43% fewer migrations than the benchmark algorithms.     

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate.     

A Unified Decision-Making Technique for Analysing Treatments inPandemicContext

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF AHP-TOPSIS Methodology, which is a well-known and highly effective Multi-Criteria Decision Making (MCDM) technique. The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries, based on the recommendations of medical practitioners and professionals. The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives. We also perform robustness analysis to validate the conclusions of our analysis. As a result, we obtained highly corroborative results that can be used as a reference. The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand, implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion. Peepli also has the lowest priority in the estimation.     

Solution‐Processed Ti3C2Tx MXene Antennas for Radio‐Frequency Communication

Highly integrated, flexible, and ultrathin wireless communication components are in significant demand due to the explosive growth of portable and wearable electronic devices in the fifth‐generation (5G) network era, but only conventional metals meet the requirements for emerging radio‐frequency (RF) devices so far. Here, it is reported on Ti₃C₂T_x MXene microstrip transmission lines with low‐energy attenuation and patch antennas with high‐power radiation at frequencies from 5.6 to 16.4 GHz. The radiation efficiency of a 5.5 µm thick MXene patch antenna manufactured by spray‐coating from aqueous solution reaches 99% at 16.4 GHz, which is about the same as that of a standard 35 µm thick copper patch antenna at about 15% of its thickness and 7% of the copper weight. MXene outperforms all other materials evaluated for patch antennas to date. Moreover, it is demonstrated that an MXene patch antenna array with integrated feeding circuits on a conformal surface has comparable performance with that of a copper antenna array at 28 GHz, which is a target frequency in practical 5G applications. The versatility of MXene antennas in wide frequency ranges coupled with the flexibility, scalability, and ease of solution processing makes MXene promising for integrated RF components in various flexible electronic devices.     

Effect of elastane linear density and draft ratio on the physical and mechanical properties of core-spun cotton yarns

The aim of this study was to investigate the effect of elastane linear density and draft ratio on the physical and mechanical properties of core-spun yarns. Twenty yarn samples were prepared on industrial scale in a spinning mill with two different yarn linear densities, each with different two elastane deniers and five draft ratios. It was found that core-spun yarn’s tenacity, elongation and hairiness are affected not only by the overall yarn linear density but also by the elastane linear density and the draft ratio. However, the effect of elastane linear density and draft ratio was not found to be statistically significant on the yarn mass variations and total imperfections, which are only affected by the overall yarn liner density. A statistically significant interaction for yarn elongation at break was found between the yarn liner density and the elastane linear density concluding that elastane linear density used in the core must be compatible with the overall yarn liner density for attaining the best yarn elongation.     

Undesirable Enzymatic Browning in Crustaceans: Causative Effects and Its Inhibition by Phenolic Compounds

Undesirable enzymatic browning mediated by polyphenol oxidase (E.C. 1.14.18.1) on the surface of seafood from crustaceans have been a great concern to food processors, causing quality losses of seafood products. Seafoods especially from crustaceans are worldwide consumed due to their delicacy and nutritional value. However, black spot formation (melanosis) is the major problem occurring in crustaceans during postmortem handling and refrigerated storage induce deleterious changes in organoleptic properties and, therefore, decreases commercial value. Polyphenoloxidase (PPO), the copper-containing metalloprotein involved in oxidation of phenol to quinone is the major biochemical reaction of melanosis formation. This enzymatic mechanism causes unappealing blackening in postharvest crustaceans. To alleviate the melanosis formation in crustaceans, use of phenolic compounds from plant extract can serve as antimelanotics and appears to be a good alternative to the conventional sulfites which are associated with health-related disorders. In this review, we focuses on the unique features about the structure, distribution, and properties of PPO as well as mechanism of melanosis formation and provide a comprehensive deeper insight on the factors affecting melanosis formation and its inhibition by various antimelanotics including newly discovered plant phenolic compounds.     

Smart packaging: sensors for monitoring of food quality and safety

The development of chemical sensors and biosensors over several decades has been investigated resulting in novel and very interesting sensor devices with great promise for many areas of applications including food technology. The incorporation of such sensors into the food packaging technology has resulted what we call smart or intelligent packaging. These are truly integrated and interdisciplinary systems that invoke expertise from the fields of chemistry, biochemistry, physics and electronics as well as food science and technology. Smart packaging utilises chemical sensor or biosensor to monitor the quality & safety of food from the producers to the costumers. This technology can result in a variety of sensor designs that are suitable for monitoring of food quality and safety, such as freshness, pathogens, leakage, carbon dioxide, oxygen, pH, time or temperature. Thus, this technology is needed as on-line quality control and safety in term of consumers, authorities and food producers, and has great potential in the development of new sensing systems integrated in the food packaging, which are beyond the existing conventional technologies, like control of weight, volume, colour and appearance.