Analysis Techniques
Recently Published Documents


TOTAL DOCUMENTS

7898
(FIVE YEARS 4250)

H-INDEX

82
(FIVE YEARS 24)

2022 ◽  
Author(s):  
Cristian Monea ◽  
Nicu Bizon

Author(s):  
Mustafa Abumeeiz ◽  
Lauren Elliott ◽  
Phillip Olla

Abstract Due to the COVID-19 pandemic, there is currently a need for accurate, rapid, and easy-to-administer diagnostic tools to help communities manage local outbreaks and assess the spread of disease. The use of Artificial Intelligence within the domain of breath analysis techniques has shown to have potential in diagnosing a variety of diseases such as cancer and lung disease by analyzing volatile organic compounds (VOCs) in exhaled breath. This combined with their rapid, easy-to-use, and non-invasive nature makes them a good candidate for use in diagnosing COVID-19 in large scale public health operations. However, there remains issues with their implementation when it comes to the infrastructure currently available to support their use on a broad scale. This includes issues of standardization, and whether or not a characteristic VOC pattern can be identified for COVID-19. Despite these difficulties, breathalysers offer potential to assist in pandemic responses and their use should be investigated.


2021 ◽  
Vol 5 (3) ◽  
pp. 176-183
Author(s):  
Martoyo Martoyo ◽  
Herlan Herlan ◽  
Nahot Tua Parlindungan Sihaloho ◽  
Deni Darmawan

This study aims to analyze the strategy of the Singkawang City Government in restoring the private sector during the COVID-19 pandemic. The research method is descriptive and qualitative, specifically related to the COVID-19 impact mitigation strategy and Singkawang tourism policies. Then analyze the strategic elements of a policy according to the implementation of the O.Jones model. Data was collected by using interviews, observation, and documentation techniques. The data is then analyzed using domain data analysis techniques as a researcher's effort to get a general and comprehensive (holistic) picture of the object under study. The findings in this study are 1) a connected tourism COVID-19 impact mitigation policy strategy from the national to local levels in Singkawang in the form of directives, regulations, COVID-19 mitigation programs, and stimulus for economic recovery in the tourism sector; and 2) there are no visible creative efforts based on health protocols in organizing tourism resources, methods, and unit synergies to restore tourism, interpretation of social media-based policies regarding tourist visits has not been measured, and strategies have not been implemented to meet the opportunities for pandemic trend tourism types with models alternative tourism according to health protocols.


2021 ◽  
Author(s):  
Zirui Liu ◽  
Binfeng Liu ◽  
Hao Yang ◽  
Liang Zhao

Abstract Objective: The purpose of this present study was to estimate complication and other outcomes associated with staples and sutures closure after hip arthroplasty through meta- analysis techniques and system review. Methods: We searched for articles on EMBASE, PubMed, Medline, Web of Science and Cochrane Library. Eligibility of the searched trials. Cochrane Collaboration's Review Manager software is used to perform meta-analysis.Results: Four randomized controlled trials and one retrospective cohort trial chosen into our study. Our study indicated that the risk of infection and prolonged discharge higher with staples than with sutures for skin closure after hip arthroplasty. Meanwhile, there was no significant difference in allergic reaction, dehiscence, inflammation, abscess formation, Hollander Wound Evaluation Score and patient's satisfaction with skin closure methods between the two groups after hip arthroplasty. However, the suture group may require additional operating time.Conclusions: Closure with suture have a lower risk of infection and prolonged discharge when compared with staples skin closure in hip arthroplasty, while it may take more time.


2021 ◽  
Vol 12 ◽  
Author(s):  
Tiffany Shi ◽  
Krishna Roskin ◽  
Brian M. Baker ◽  
E. Steve Woodle ◽  
David Hildeman

Solid organ transplant recipients require long-term immunosuppression for prevention of rejection. Calcineurin inhibitor (CNI)-based immunosuppressive regimens have remained the primary means for immunosuppression for four decades now, yet little is known about their effects on graft resident and infiltrating immune cell populations. Similarly, the understanding of rejection biology under specific types of immunosuppression remains to be defined. Furthermore, development of innovative, rationally designed targeted therapeutics for mitigating or preventing rejection requires a fundamental understanding of the immunobiology that underlies the rejection process. The established use of microarray technologies in transplantation has provided great insight into gene transcripts associated with allograft rejection but does not characterize rejection on a single cell level. Therefore, the development of novel genomics tools, such as single cell sequencing techniques, combined with powerful bioinformatics approaches, has enabled characterization of immune processes at the single cell level. This can provide profound insights into the rejection process, including identification of resident and infiltrating cell transcriptomes, cell-cell interactions, and T cell receptor α/β repertoires. In this review, we discuss genomic analysis techniques, including microarray, bulk RNAseq (bulkSeq), single-cell RNAseq (scRNAseq), and spatial transcriptomic (ST) techniques, including considerations of their benefits and limitations. Further, other techniques, such as chromatin analysis via assay for transposase-accessible chromatin sequencing (ATACseq), bioinformatic regulatory network analyses, and protein-based approaches are also examined. Application of these tools will play a crucial role in redefining transplant rejection with single cell resolution and likely aid in the development of future immunomodulatory therapies in solid organ transplantation.


Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 118
Author(s):  
Luciano Ortenzi ◽  
Simona Violino ◽  
Federico Pallottino ◽  
Simone Figorilli ◽  
Simone Vasta ◽  
...  

Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Brady Lund ◽  
Jinxuan Ma

PurposeThis literature review explores the definitions and characteristics of cluster analysis, a machine-learning technique that is frequently implemented to identify groupings in big datasets and its applicability to library and information science (LIS) research. This overview is intended for researchers who are interested in expanding their data analysis repertory to include cluster analysis, rather than for existing experts in this area.Design/methodology/approachA review of LIS articles included in the Library and Information Source (EBSCO) database that employ cluster analysis is performed. An overview of cluster analysis in general (how it works from a statistical standpoint, and how it can be performed by researchers), the most popular cluster analysis techniques and the uses of cluster analysis in LIS is presented.FindingsThe number of LIS studies that employ a cluster analytic approach has grown from about 5 per year in the early 2000s to an average of 35 studies per year in the mid- and late-2010s. The journal Scientometrics has the most articles published within LIS that use cluster analysis (102 studies). Scientometrics is the most common subject area to employ a cluster analytic approach (152 studies). The findings of this review indicate that cluster analysis could make LIS research more accessible by providing an innovative and insightful process of knowledge discovery.Originality/valueThis review is the first to present cluster analysis as an accessible data analysis approach, specifically from an LIS perspective.


2021 ◽  
Vol 23 ◽  
Author(s):  
Maren Liese Jorgensen

As the population of elderly adults continues to rise, a greater strain will be placed on the healthcare system. Functional exercise programs, such as the 3-Step Workout for Life, have been shown to improve activities of daily living and delay the disablement process. However, the majority of senior living communities do not utilize functional exercise in their fitness programming. This research study aimed to understand the perceptions that fitness staff working at senior living communities have towards the 3-Step Workout for Life program in order to determine the program’s acceptability, feasibility, and appeal. Individual semi-structured interviews were conducted with five fitness personnel. Participants were recruited from independent living communities. Interviews were audio-recorded and transcribed. Using NVivo 12, data was analyzed using thematic analysis techniques to identify common themes. The participants’ perceptions touch on four key themes: 1) revision of screening process; 2) group resistance band exercise would be feasible after minor adaptation; 3) individualized one-on-one ADL exercise is not currently feasible for staff or residents; 4) program addresses a gap in senior fitness. The results of this study provided insight into the feasibility of this program and helped direct modifications needed to enable successful integration.


Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6775
Author(s):  
Vishnu Manasa Devagiri ◽  
Veselka Boeva ◽  
Shahrooz Abghari ◽  
Farhad Basiri ◽  
Niklas Lavesson

In smart buildings, many different systems work in coordination to accomplish their tasks. In this process, the sensors associated with these systems collect large amounts of data generated in a streaming fashion, which is prone to concept drift. Such data are heterogeneous due to the wide range of sensors collecting information about different characteristics of the monitored systems. All these make the monitoring task very challenging. Traditional clustering algorithms are not well equipped to address the mentioned challenges. In this work, we study the use of MV Multi-Instance Clustering algorithm for multi-view analysis and mining of smart building systems’ sensor data. It is demonstrated how this algorithm can be used to perform contextual as well as integrated analysis of the systems. Various scenarios in which the algorithm can be used to analyze the data generated by the systems of a smart building are examined and discussed in this study. In addition, it is also shown how the extracted knowledge can be visualized to detect trends in the systems’ behavior and how it can aid domain experts in the systems’ maintenance. In the experiments conducted, the proposed approach was able to successfully detect the deviating behaviors known to have previously occurred and was also able to identify some new deviations during the monitored period. Based on the results obtained from the experiments, it can be concluded that the proposed algorithm has the ability to be used for monitoring, analysis, and detecting deviating behaviors of the systems in a smart building domain.


2021 ◽  
Author(s):  
Artur Mihailovich Aslanyan ◽  
Bulat Galievich Ganiev ◽  
Azat Abuzarovich Lutfullin ◽  
Ildar Zufarovich Farkhutdinov ◽  
Marat Yurievich Garnyshev ◽  
...  

Abstract The paper presents a practical case of production performance analysis at one of the mature waterflood oil fields located at the Volga-Ural oil basin with a large number of wells. It is a big challenge to analyse such a large production history and requires a systematic approach. The main production complication is quite common for mature waterflood projects and includes non-uniform sweep, complicated by thief injection and thief water production. The main challenge is to locate the misperforming wells and address their complications. With the particular asset, the conventional single production analysis techniques (oil production trend, watercut trend, reservoir and bottom-hole pressure trend, productivity trend, conventional pressure build-up surveys and production logging) in the vast majority of cases were not capable of qualifying the well performance and assessing of remaining reserves status. The performance analysis of such an asset should be enhanced with new diagnostic tools and modern methods of data integration. The current study has made a choice in favor of using a PRIME analysis which is multi-parametric analytical workflow based on a set of conventional and non-conventional diagnostic metrics. The most effective diagnostics in this study have happened to be those are based on 3D dynamic micro-models, which are auto-generated from the reservoir data logs. PRIME also provided useful insights on well performance, formation properties and the current conditions of drained reserves which helped to select the candidates for infill drilling, pressure maintenance, workovers, production target adjustments and additional surveillance. The paper illustrates the entire PRIME workflow, starting from the top-level field data analysis, all the way to generating a summary table containing well diagnostics, justifications and recommendations.


Sign in / Sign up

Export Citation Format

Share Document