Scope of Improvements in Aerospace Sector by Implementation of IoT

While considering the aerospace domain, the internet of things (IoT) provides the way for new development and this IoT technology allows many possibilities in the aerospace domain. This study aims to examine the theoretical aspect of IoT in the aerospace industry. And propose a system that enhances the flight journey experience from the flight booking of each customer. This will also improve the manufacturing end-to-end process in the aerospace industry with the help of IoT sensors. These can be achieved with help of the data collection (Previous sensor data), cloud computing, and machine learning. As per the proposing system, all IoT sensor data will be collected and saved the data in the cloud server. These data will be used for training the algorithm to achieve the optimum solution in the future.

The functio-spatial structure of airport surroundings: the case of Kraków Airport

The area around Krakow airport is an attractive developmental urban area. The concentration of passenger traffic and the flow of goods is conducive to new development projects. These projects are associated with the operation of airports, as well as new uses which see the proximity of an airport as an additional asset in operating a business based on access to a form of high-speed transport. This paper presents the findings of research concerning the existing spatial structure, transport accessibility and compositional determinants within an area around Krakow Airport, which can be used to assess the phenomena present and formulate principles and trajectories of shaping them in the future. The study was based on an analysis of selected elements of the existing functio-spatial structure, as well as available materials and subject-specific planning documents. Due to the specificity of areas around airports, which undergo dynamic change, it appears key to determine the individual form of development and land cover, that skilfully combines modernity and comfort of use with meshing with the local landscape, featuring a network of linkages and the character of suburban space. The issues present in this area are distinctive of many cities and require coherent land development proposals.

A unique Thy-1-negative immuno-fibroblast population emerges as a key determinant of fibrotic outcomes to biomaterials

Microporous annealed particle (MAP) hydrogels are an exciting new development in biomaterial design. They regulate innate and acquired immunity which has been linked to their ability to evade normal host-material fibrosis. Yet, resident stromal fibroblasts, not immune cells, are the arbiters of the extracellular matrix assembly that characterizes fibrosis. In other idiopathic fibrotic disorders, a fibroblast subpopulation defined by its loss of cell surface Thy-1 expression is strongly correlated with degree of fibrosis. We have previously shown that Thy-1 is a critical αvβ3 integrin regulator that enables normal fibroblast mechanosensing and here, leveraging non-fibrosing MAP gels, we demonstrate that Thy-1-/- mice mount a robust response to MAP gels that remarkably resembles a classical foreign body response. We further find that within the naive, Thy-1+ fibroblast population exists a distinct and cryptic αSMA+ Thy-1- population that emerges in response to IL-1β and TNFα. Employing single-cell RNA sequencing, we find that IL-1β/TNFα-induced Thy-1- fibroblasts actually consist of two distinct subpopulations, both of which are strongly pro-inflammatory. These findings illustrate the emergence of a unique pro-inflammatory, pro-fibrotic fibroblast subpopulation that is central to material-associated fibrosis likely through amplifying local inflammatory signaling.

New Trends in Melanoma Detection Using Neural Networks: A Systematic Review

Due to its increasing incidence, skin cancer, and especially melanoma, is a serious health disease today. The high mortality rate associated with melanoma makes it necessary to detect the early stages to be treated urgently and properly. This is the reason why many researchers in this domain wanted to obtain accurate computer-aided diagnosis systems to assist in the early detection and diagnosis of such diseases. The paper presents a systematic review of recent advances in an area of increased interest for cancer prediction, with a focus on a comparative perspective of melanoma detection using artificial intelligence, especially neural network-based systems. Such structures can be considered intelligent support systems for dermatologists. Theoretical and applied contributions were investigated in the new development trends of multiple neural network architecture, based on decision fusion. The most representative articles covering the area of melanoma detection based on neural networks, published in journals and impact conferences, were investigated between 2015 and 2021, focusing on the interval 2018–2021 as new trends. Additionally presented are the main databases and trends in their use in teaching neural networks to detect melanomas. Finally, a research agenda was highlighted to advance the field towards the new trends.

Radionuclides in Diagnostics and Therapy of Malignant Tumors: New Development

The interest in using targeted radiopharmaceuticals in nuclear oncology has increased in recent years and continues to grow [...]

High-Sensitivity PtSe2 Surface Plasmon Resonance Biosensor Based on Metal-Si-Metal Waveguide Structure

PtSe2 as a novel TMDCs material is used to modify the traditional SPR biosensors to improve the performance. On this basis, this research proposes a metal-Si-metal waveguide structure to further improve the performance of the biosensor. In this study, we not only studied the effects of waveguide structures containing different metals on the performance of biosensor, but also discussed the performance change of the biosensor with the change of PtSe2 thickness. After the final optimization, a BK7-Au-Si-Au-PtSe2 (2 nm) biosensor structure achieved the highest sensitivity of 193.8°/RIU. This work provides a new development idea for the study of SPR biosensors with waveguide structures in the future.

Experimental Research of Compound Monitoring on Multiple Temporary Blocking Refracturing for Long-Section Horizontal Wells

As an important energy replacement block in China, the tight conglomerate oilfields in the Mahu area are difficult to develop and are characterized by strong heterogeneity, large horizontal stress differences, and undeveloped natural fractures. However, new development processes including temporary blocking diversion and large section-multiple clusters have been implemented on the oilfields in the past few years. In 2020, two adjacent horizontal wells in the MD well area experienced a poor fracturing development effect compared with the earlier wells in this area. Analysis suggests that the main reasons are water sensitivity of the reservoir, insufficient fracturing scale, and/or interference from the adjacent old wells. To ameliorate the problem, this study presents an experimental study of multiple temporary plugging and refracturing technology in long horizontal well sections, in combination with electromagnetic and microseismic monitoring. Results from the study show a great difference between the two monitoring techniques, which is attributed to their different detection principles. Interestingly, the combination of the two approaches provides a greater performance than either approach alone. As the fracturing fluid flow diversion is based on temporary plugging diversion and electromagnetic monitoring of fracturing fluid is advantageous in temporary plugging diversion monitoring, both approaches require further research and development to address complex situations such as multiple temporary plugging and refracturing in long intervals of adjacent older wells.

The Future of Digital Tourism Alternatives in Virtual Reality

In this study, a review of the future of VR technologies for digital tourism alternatives will be presented. The general purpose of the research is to contribute to VR technologies, which are developing and integrating with new systems with each passing day from the point of view of providing a general perspective in tourism. VR application information to deepen understanding of the scope of the digital future of tourism alternatives will be presented in a systematic framework. In general, VR technologies express the behavioral experiences of individuals in virtual environments. When tourism and VR technologies are combined, the composition of the product in which these virtual experiences are directed towards a purpose emerges. Every new development that occurs from a technological point of VR technology will make it possible to be used in every field of tourism. The contribution of this research is to provide suggestions for future studies as well as practical implications for the tourism industry regarding VR systems, which are among the digital tourism alternatives.

Research on multi-view clustering algorithm on epileptic EEG signal

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.