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1
Hsu, Ming-Hung, Yu-Hui Chang, and Hsin-Hsi Chen. "Temporal Correlation between Social Tags and Emerging Long-Term Trend Detection." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (May 16, 2010): 255–58. http://dx.doi.org/10.1609/icwsm.v4i1.14049.
Повний текст джерелаАнотація:
Social annotation has become a popular manner for web users to manage and share their information and interests. While users' interests vary with time, tag correlation also changes from users' perspectives. In this work, we explore four methods for estimating temporal correlation between social tags and detect if a long-term trend emerges from the history of temporal correlation between two tags. Three types of trends are specified: steadily-shifting, stabilizing, and cyclic. To compare the results of the four estimation methods, an indirect evaluation is realized by applying detected trends to tag recommendation.
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VALENCIA, MARIA, CODRINA LAUTH, and ERNESTINA MENASALVAS. "EMERGING USER INTENTIONS: MATCHING USER QUERIES WITH TOPIC EVOLUTION IN NEWS TEXT STREAMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, supp01 (August 2009): 59–80. http://dx.doi.org/10.1142/s0218488509006030.
Повний текст джерелаАнотація:
Trend detection analysis from unstructured data poses a huge challenge to current advanced, web-enabled knowledge-based systems (KBS). Consolidated studies in topic and trend detection from text streams have concentrated so far mainly on identifying and visualizing dynamically evolving text patterns. From the knowledge modeling perspective identifying and defining new, relevant features that are able to synchronize the emergent user intentions to the dynamicity of the system's structure is a need. Additionally the advanced KBS have to remain highly sensitive to the content change, marked by evolution of trends in topics extracted from text streams. In this paper, we are describing a three-layered approach called the "user-system-content method" that is helping us to identify the most relevant knowledge mapping features derived from the USER, SYSTEM and CONTENT perspectives into an overall "context model", that will enable the advanced KBS to automatically streamline the query enrichment process in a much more user-centered, dynamical and flexible way. After a general introduction to our three-layered approach, we will describe into detail the necessary process steps for the implementation of our method and will present a case study for its integration on a real multimedia web-content portal using news streams as major source of unstructured information.
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Lackner, Bettina C., Andrea K. Steiner, Gabriele C. Hegerl, and Gottfried Kirchengast. "Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method." Journal of Climate 24, no. 20 (October 15, 2011): 5275–91. http://dx.doi.org/10.1175/2011jcli3966.1.
Повний текст джерелаАнотація:
Abstract The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere–lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995–2010 intermittently and 2001–10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 50°S–50°N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.
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Wu, Yuqi, Yuhan Deng, Longgang Zhang, Qidong Zhang, and Ran Bao. "Research on the Development of Unmanned Underwater System Detection Technology." Journal of Physics: Conference Series 2218, no. 1 (March 1, 2022): 012079. http://dx.doi.org/10.1088/1742-6596/2218/1/012079.
Повний текст джерелаАнотація:
Abstract In recent years, the number of unmanned systems in the world has been increasing. The emerging underwater platforms such as UUV are large in number, small in size, low in speed and low in noise. In the future, a variety of ocean combat missions will use a large number of UUV platforms. The existing methods for detecting underwater vehicles mainly include sonar, magnetometer. Based on this, by referring to a large number of literatures, a review of the research progress of underwater exploration at home and abroad in recent years. And compared with the United States, Russia and other countries unmanned system detection technology and equipment, analysis of their applicable conditions and advantages and disadvantages. This paper summarizes the technical difficulties of underwater detection methods and proposes corresponding solutions. At the same time, we forecast the development trend of underwater detection technology in the future.
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SUCHIT K. RAI, SUNIL KUMAR, and MANOJ CHAUDHARY. "Detection of annual and seasonal temperature variability and change using non-parametric test- A case study of Bundelkhand region of central India." Journal of Agrometeorology 23, no. 4 (November 11, 2021): 402–8. http://dx.doi.org/10.54386/jam.v23i4.144.
Повний текст джерелаАнотація:
Consequences of global warming and climate change are major threat to humans and their socio-economic activities. Agriculture of Bundelkhand region is supposed to be more vulnerable due to emerging scenario of climate change and poor socio-economic status of farming community. Many studies carried out elsewhere have shown evidence of regional temperature variability along with global climate changes. This study focuses on the temporal variability and trend in annual and seasonal temperature (1901-2012) at six locations of Bundelkhand region. The results of the analysis reveal that the annual maximum (TMax) and minimum (TMin) temperature has significantly increasing trend in all the locations in the range of 0.5 to 2.0oC 100 year-1 and 0.5 to 1.1 oC 100 year-1, respectively. Seasonal analysis revealed warming trend in both TMax (0.6-2.6oC100 year-1) and TMin (0.9 to 2.3 oC 100 year-1) during post-monsoon and winter season in all the locations. Majority of the locations showed cooling trend (0.3-1.0 oC 100 year-1), in the mean maximum and minimum temperature during monsoon season except at two locations i.e Jhansi and Banda. However, a significant positive trends (2.9 oC) in the TMin was found for the period of hundred years at Banda district during monsoon season.
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Parlina, Anne, Kalamullah Ramli, and Hendri Murfi. "Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means." Sustainability 13, no. 5 (March 7, 2021): 2876. http://dx.doi.org/10.3390/su13052876.
Повний текст джерелаАнотація:
The literature discussing the concepts, technologies, and ICT-based urban innovation approaches of smart cities has been growing, along with initiatives from cities all over the world that are competing to improve their services and become smart and sustainable. However, current studies that provide a comprehensive understanding and reveal smart and sustainable city research trends and characteristics are still lacking. Meanwhile, policymakers and practitioners alike need to pursue progressive development. In response to this shortcoming, this research offers content analysis studies based on topic modeling approaches to capture the evolution and characteristics of topics in the scientific literature on smart and sustainable city research. More importantly, a novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend. The topics generated by this proposed algorithm have relatively higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and eigenspace-based fuzzy C-means (EFCM). The 30 main topics that appeared in topic modeling with the DFCM algorithm were classified into six groups (technology, energy, environment, transportation, e-governance, and human capital and welfare) that characterize the six dimensions of smart, sustainable city research.
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Perisic, Marija Majda, Mario Štorga, and John S. Gero. "COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK." Proceedings of the Design Society 1 (July 27, 2021): 691–700. http://dx.doi.org/10.1017/pds.2021.69.
Повний текст джерелаАнотація:
AbstractWhen observing a design space expansion during teamwork, several studies found that cumulative solution-related issues' occurrence follows a linear trend. Such findings contradict the hypothesis of solution-related issues being characteristic for the later design stages. This work relies on agent-based simulations to explore the emerging patterns in design solution space expansion during teamwork. The results demonstrate trends that accord with the empirical findings, suggesting that a cognitive effort in solution space expansion remains constant throughout a design session. The collected data on agents' cognitive processes and solution space properties enabled additional insights, which led to the detection of four distinct regimes of design solution space expansion.
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Salunkhe, Uma R., and Suresh N. Mali. "Security Enrichment in Intrusion Detection System Using Classifier Ensemble." Journal of Electrical and Computer Engineering 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1794849.
Повний текст джерелаАнотація:
In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.
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Katsurai, Marie, and Shunsuke Ono. "TrendNets: mapping emerging research trends from dynamic co-word networks via sparse representation." Scientometrics 121, no. 3 (October 18, 2019): 1583–98. http://dx.doi.org/10.1007/s11192-019-03241-6.
Повний текст джерелаАнотація:
Abstract Mapping the knowledge structure from word co-occurrences in a collection of academic papers has been widely used to provide insight into the topic evolution in an arbitrary research field. In a traditional approach, the paper collection is first divided into temporal subsets, and then a co-word network is independently depicted in a 2D map to characterize each period’s trend. To effectively map emerging research trends from such a time-series of co-word networks, this paper presents TrendNets, a novel visualization methodology that highlights the rapid changes in edge weights over time. Specifically, we formulated a new convex optimization framework that decomposes the matrix constructed from dynamic co-word networks into a smooth part and a sparse part: the former represents stationary research topics, while the latter corresponds to bursty research topics. Simulation results on synthetic data demonstrated that our matrix decomposition approach achieved the best burst detection performance over four baseline methods. In experiments conducted using papers published in the past 16 years at three conferences in different fields, we showed the effectiveness of TrendNets compared to the traditional co-word representation. We have made our codes available on the Web to encourage scientific mapping in all research fields.
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Schaffhauser, Andreas, Wojciech Mazurczyk, Luca Caviglione, Marco Zuppelli, and Julio Hernandez-Castro. "Efficient Detection and Recovery of Malicious PowerShell Scripts Embedded into Digital Images." Security and Communication Networks 2022 (June 29, 2022): 1–12. http://dx.doi.org/10.1155/2022/4477317.
Повний текст джерелаАнотація:
Due to steady improvements in defensive systems, malware developers are turning their attention to mechanisms for cloaking attacks as long as possible. A recent trend exploits techniques like Invoke-PSImage, which allows embedding a malicious script within an innocent-looking image, for example, to smuggle data into compromised devices. To address such a class of emerging threats, new mechanisms are needed, since standard tools fail in their detection or offer poor performance. To this aim, this work introduces Mavis, an efficient and highly accurate method for detecting hidden payloads, retrieving the embedded information, and estimating its size. Experimental results collected by considering real-world malicious PowerShell scripts showcase that Mavis can detect attacks with a high accuracy (100%) while keeping the rate of false positives and false negatives very low (0.01% and 0%, respectively). The proposed approach outperforms other solutions available in the literature or commercially through “as a service” model.
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Patel, Hitika, Deepak Rawtani, and Y. K. Agrawal. "A newly emerging trend of chitosan-based sensing platform for the organophosphate pesticide detection using Acetylcholinesterase- a review." Trends in Food Science & Technology 85 (March 2019): 78–91. http://dx.doi.org/10.1016/j.tifs.2019.01.007.
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Ciernikova, Sona, Maria Novisedlakova, Danka Cholujova, Viola Stevurkova, and Michal Mego. "The Emerging Role of Microbiota and Microbiome in Pancreatic Ductal Adenocarcinoma." Biomedicines 8, no. 12 (December 3, 2020): 565. http://dx.doi.org/10.3390/biomedicines8120565.
Повний текст джерелаАнотація:
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignant tumors due to the absence of biomarkers for early-stage detection and poor response to therapy. Since mounting evidence supports the role of microbiota composition in tumorigenesis and cancer treatment, the link between microbiome and PDAC has been described. In this review, we summarize the current knowledge regarding the impact of the gut and oral microbiome on the risk of PDAC development. Microenvironment-driven therapy and immune system interactions are also discussed. More importantly, we provide an overview of the clinical trials evaluating the microbiota role in the risk, prognosis, and treatment of patients suffering from PDAC and solid tumors. According to the research findings, immune tolerance might result from the microbiota-derived remodeling of pancreatic tumor microenvironment. Thus, microbiome profiling and targeting represent the potential trend to enhance antitumor immunity and improve the efficacy of PDAC treatment.
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Schmidt, Alison, and Anthony Shanks. "Quadruple Screening in the Age of Cell-Free DNA: What are We Losing?" OBM Genetics 05, no. 03 (June 16, 2021): 1. http://dx.doi.org/10.21926/obm.genet.2103138.
Повний текст джерелаАнотація:
Cell-free DNA has emerged as the most reliable, non-invasive prenatal screening tool for fetal aneuploidies. It has come to replace the previously widely used quadruple screen offered in the second trimester of pregnancy. This change comes with improved detection for aneuploidy but also presents potential gaps in prenatal diagnosis including detection of open fetal defects and emerging data on prediction of adverse pregnancy outcomes. This review article provides a historical summary of the quadruple marker screen and evaluates the intersection of this screen with cell-free DNA. Furthermore, it discusses points to consider as providers trend toward cell-free DNA testing alone and reviews potential options to remedy any disparities.
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Shao, Meng. "Measurement and Trend Analysis of New Media Coverage Topics Based on Comment Big Data Mining." Mathematical Problems in Engineering 2022 (July 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/1404468.
Повний текст джерелаАнотація:
With the rapid development of Internet, new media news has gradually become the most concerned information source of new media people. New media public opinion is a force that cannot be ignored. It needs monitoring and guidance. The research on hot topic discovery and trend analysis can timely find social hot topics and analyze the trend of topics, which is conducive to grasp the trend of public opinion, so as to correctly guide and maintain social stability. At the same time, the emerging industry of new media has sprung up like mushrooms after a rain, with a rapid momentum. With the advent of the era of big data, the development of new media presents characteristics and advantages that are different from traditional media, but we should also note that the era of big data has both advantages and disadvantages for the development of new media. In this paper, LDA and ARIMA models can be used to calculate and analyze the popularity measurement and trend analysis of new media reports under the background of big data mining. The model designed in this paper has a conclusion: the missed detection rate is reduced by 75.4%. Through experiments, it is found that the accuracy of heat topic detection of the model designed in this paper can reach 84.6%. In trend analysis, the first stage of transmission is called the incubation period. Then after a certain critical point, it will come to the outbreak period. The outbreak period lasted for a period of time, entered a period of plateau, and finally came to a subsidence period.
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Mueller, B. L., N. P. Gillett, A. H. Monahan, and F. W. Zwiers. "Attribution of Arctic Sea Ice Decline from 1953 to 2012 to Influences from Natural, Greenhouse Gas, and Anthropogenic Aerosol Forcing." Journal of Climate 31, no. 19 (October 2018): 7771–87. http://dx.doi.org/10.1175/jcli-d-17-0552.1.
Повний текст джерелаАнотація:
The paper presents results from a climate change detection and attribution study on the decline of Arctic sea ice extent in September for the 1953–2012 period. For this period three independently derived observational datasets and simulations from multiple climate models are available to attribute observed changes in the sea ice extent to known climate forcings. Here we direct our attention to the combined cooling effect from other anthropogenic forcing agents (mainly aerosols), which has potentially masked a fraction of greenhouse gas–induced Arctic sea ice decline. The presented detection and attribution framework consists of a regression model, namely, regularized optimal fingerprinting, where observations are regressed onto model-simulated climate response patterns (i.e., fingerprints). We show that fingerprints from greenhouse gas, natural, and other anthropogenic forcings are detected in the three observed records of Arctic sea ice extent. Beyond that, our findings indicate that for the 1953–2012 period roughly 23% of the greenhouse gas–induced negative sea ice trend has been offset by a weak positive sea ice trend attributable to other anthropogenic forcing. We show that our detection and attribution results remain robust in the presence of emerging nonstationary internal climate variability acting upon sea ice using a perfect model experiment and data from two large ensembles of climate simulations.
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Kam, Jonghun, Thomas R. Knutson, and P. C. D. Milly. "Climate Model Assessment of Changes in Winter–Spring Streamflow Timing over North America." Journal of Climate 31, no. 14 (July 2018): 5581–93. http://dx.doi.org/10.1175/jcli-d-17-0813.1.
Повний текст джерелаАнотація:
Over regions where snowmelt runoff substantially contributes to winter–spring streamflows, warming can accelerate snowmelt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by the brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, the detection/attribution of changes in midlatitude North American winter–spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. Robustness across models, start/end dates for trends, and assumptions about internal variability are evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central United States, where winter–spring streamflows have been starting earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western United States/southwestern Canada and in the extreme northeastern United States/Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.
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Zhang, Zhicheng, Ismail Fidan, and Michael Allen. "Detection of Material Extrusion In-Process Failures via Deep Learning." Inventions 5, no. 3 (July 1, 2020): 25. http://dx.doi.org/10.3390/inventions5030025.
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Additive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and problem-solving aspects of AM processes. Deep learning (DL) is an emerging machine learning (ML) type that has widely been used in several research studies. This research team believes that applying DL can help make AM processes smoother and make AM-printed objects more accurate. In this research, a new DL application is developed and implemented to minimize the material consumption of a failed print. The material used in this research is polylactic acid (PLA) and the DL method is the convolutional neural network (CNN). This study reports the nature of this newly developed DL application and the relationships between various algorithm parameters and the accuracy of the algorithm.
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Tripathi, Tripti, and Rakesh Kumar. "Performance Comparison of Machine Learning Algorithms for Dementia Progression Detection." International Journal of Software Science and Computational Intelligence 14, no. 1 (January 1, 2022): 1–18. http://dx.doi.org/10.4018/ijssci.312553.
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Dementia is a neurological disease that that encompasses a wide range of conditions like verbal communication, problem-solving, and other judgment abilities that are severely sufficient to interfere with daily life. It is among the leading causes of vulnerability among the elderly all over the world. A considerable amount of research has been conducted in this area so that we can perform early detection of the disease, yet further research into its betterment is still an emerging trend. This article compares the performance of multiple machine learning models for dementia detection and classification using brain MRI data, including support vector machine, random forest, AdaBoost, and XGBoost. Meanwhile, the research conducts a systematic assessment of papers for the clinical categorization of dementia using ML algorithms and neuroimaging data. The authors used 373 participants from the OASIS database. Among the tested models, RF model exhibited the best performance with 83.92% accuracy, 87.5% precision, 81.67% recall, 84.48% F1-score, 81.67% sensitivity, and 88.46% specificity.
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Xu, Meng-Lei, Yu Gao, Xiao Wang, Xiao Xia Han, and Bing Zhao. "Comprehensive Strategy for Sample Preparation for the Analysis of Food Contaminants and Residues by GC–MS/MS: A Review of Recent Research Trends." Foods 10, no. 10 (October 15, 2021): 2473. http://dx.doi.org/10.3390/foods10102473.
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Food safety and quality have been gaining increasing attention in recent years. Gas chromatography coupled to tandem mass spectrometry (GC–MS/MS), a highly sensitive technique, is gradually being preferred to GC–MS in food safety laboratories since it provides a greater degree of separation on contaminants. In the analysis of food contaminants, sample preparation steps are crucial. The extraction of multiple target analytes simultaneously has become a new trend. Thus, multi-residue analytical methods, such as QuEChERs and adsorption extraction, are fast, simple, cheap, effective, robust, and safe. The number of microorganic contaminants has been increasing worldwide in recent years and are considered contaminants of emerging concern. High separation in MS/MS might be, in certain cases, favored to sample preparation selectivity. The ideal sample extraction procedure and purification method should take into account the contaminants of interest. Moreover, these methods should cooperate with high-resolution MS, and other sensitive full scan MSs that can produce a more comprehensive detection of contaminants in foods. In this review, we discuss the most recent trends in preparation methods for highly effective detection and analysis of food contaminants, which can be considered tools in the control of food quality and safety.
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Akhtar, Shahid Mehmood, and Javed Iqbal. "Assessment of emerging hydrological, water quality issues and policy discussion on water sharing of transboundary Kabul River." Water Policy 19, no. 4 (March 25, 2017): 650–72. http://dx.doi.org/10.2166/wp.2017.119.
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Transboundary water sharing policy between Pakistan and Afghanistan along with emerging issues over the Transboundary Kabul River have been discussed incorporating long-term hydrological trend analysis, water quality issues and temporal changes in land cover/land use. The annual (1977–2015) mean river flow of 26.32 billion (109) cubic metres (BCM) with a range of 13.77 to 42.2 BCM and standard deviation of 6.026 BCM revealed no significant trend in annual inflow data of the Kabul River. Afghanistan planned developments in the basin were analysed in the light of reduction in the transboundary flow. Faecal coliforms, pH (7.90 to 8.06), Escherichia coli and other water quality parameters were found to be within permissible limits, however, dissolved oxygen was just above the permissible limits to sustain aquatic life. Water was found unsuitable for drinking while suitable for agriculture and aquatic life. Remote sensing data used for temporal change detection showed an increase in built-up-areas and cultivated areas along Kabul River inside Pakistan by 50 and 47%, respectively. Significant changes were observed at two locations in the river course. Insights of emerging Kabul River issues and a way forward have been discussed which could serve as the basis for formulation of adaption strategies leading to a ‘Kabul River Water Treaty’.
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Guraya, Salman Y. "The Prevalence and Evolving Risk Factors for Colorectal Cancer in the Arab World." Biomedical and Pharmacology Journal 11, no. 4 (December 3, 2018): 1773–80. http://dx.doi.org/10.13005/bpj/1548.
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There is a global rise in the prevalence of colorectal cancer (CRC).Literature has shown an exponential growth in early detection and innovative strategies in treating CRC with some success. Despite preventive and screening measures, healthcare authorities have reported an increase in CRC in the Western as well as in the Arab world. Due to recent trend in adopting Western lifestyle by the Arab population, the current prevalence of CRC in the Arab world ranges from 19.8% to 38% and has been shown to affect younger population as well.In addition, new risk factors have been reported such as low serum levels of selenium and vitamin D, high use of food preservativesand microsatellite instability.Such emerging challenges in preventing and screening early CRC in the Arab world pose tremendous burden on healthcare authorities. In parallel with the state-of-art screening tools such as virtual colonoscopy and DNA-based stool immunohistochemistry tests, some in vivo endoscopic cytological examination by narrow band and confocal imaging have been introduced. The detection of microRNA-21 is being increasingly being used as a reliable biomarker for GIT cancers including CRC. Surgical resection of CRC remains the gold standard for CRC even in the presence of metastases.This research work underpins the prevalence of CRC globally as well as in the Arab world with special attention to emerging risk factors, early detection tools by nation-wide screening campaigns.
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Hari Krishnan Andi. "AI-Powered Drug Detection System Utilizing Bioactivity Prediction and Drug Release Tracking." December 2022 4, no. 4 (November 9, 2022): 263–73. http://dx.doi.org/10.36548/jaicn.2022.4.003.
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In recent years, Artificial Intelligence (AI) and Machine Learning technologies have played an emerging trend aiding in the creation of new medicines. Simply said, deep learning algorithms and artificial neural networks have brought a new level of sophistication to this field. In recent years, Artificial Intelligence through Machine Learning have been used in this area, and its use is supported by historical data. Additionally, freshly created modelling algorithms relied heavily on unique data mining, duration, and management strategies, which were compared to gauge overall efficiency. This paper suggests the AI powered Drug Detection System using Bioactivity Prediction and Drug Release Tracking. The experimental findings show that the suggested systems effectively recognize the illegal drug advertisements. Datasets with millions of posts gathered using the Google+ API have been used to meticulously verify both the methods. The experimental evidence shows that both approaches can be used to accurately identify medicines.
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Bagria, Vipin Kumar, and Bholi Meena. "Analysis of FinTech Enablers and Role of Future Internet of Things (IoT) in the New-Age Business World." International Journal of Management and Development Studies 12, no. 07 (July 31, 2023): 29–37. http://dx.doi.org/10.53983/ijmds.v12n07.004.
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Financial institutions work very hard to make finance smarter in the present global trend to reap the rewards of digitization. Financial technology (Fintech) uses various modern disruptive technologies, including AI, 5G/6G, Blockchain, Metaverse, IoT, and others, in the financial sector to improve client services. Many critical financial services and procedures, including lending, verification, fraud detection, quality maintenance, credit scoring, and many more, will be streamlined and improved by the advent of technology. However, there is a need for the development of innovative financial products and a supporting technological environment. To introduce ICT solutions, various tech giants have emphasized Fintech. In this book, we first imagine the emerging trends and key Fintech applications in 2030. For some use cases, we also try to present a high-level framework of the Fintech enablers, such as IoT, 5G, Digital Twins, and Metaverse. We also outline future directions for Fintech research while looking ahead to potential difficulties.
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Wu, Xiaohong, Xinyue Liang, Yixuan Wang, Bin Wu, and Jun Sun. "Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review." Foods 11, no. 22 (November 18, 2022): 3713. http://dx.doi.org/10.3390/foods11223713.
Повний текст джерелаАнотація:
With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.
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Magro, Cátia, Eduardo P. Mateus, Juan M. Paz-Garcia, Susana Sério, Maria Raposo, and Alexandra B. Ribeiro. "Electronic Tongue Coupled to an Electrochemical Flow Reactor for Emerging Organic Contaminants Real Time Monitoring." Sensors 19, no. 24 (December 4, 2019): 5349. http://dx.doi.org/10.3390/s19245349.
Повний текст джерелаАнотація:
Triclosan, which is a bacteriostatic used in household items, has raised health concerns, because it might lead to antimicrobial resistance and endocrine disorders in organisms. The detection, identification, and monitoring of triclosan and its by-products (methyl triclosan, 2,4-Dichlorophenol and 2,4,6-Trichlorophenol) are a growing need in order to update current water treatments and enable the continuous supervision of the contamination plume. This work presents a customized electronic tongue prototype coupled to an electrochemical flow reactor, which aims to access the monitoring of triclosan and its derivative by-products in a real secondary effluent. An electronic tongue device, based on impedance measurements and polyethylenimine/poly(sodium 4-styrenesulfonate) layer-by-layer and TiO2, ZnO and TiO2/ZnO sputtering thin films, was developed and tested to track analyte degradation and allow for analyte detection and semi-quantification. A degradation pathway trend was observable by means of principal component analysis, being the sample separation, according to sampling time, explained by 77% the total variance in the first two components. A semi-quantitative electronic tongue was attained for triclosan and methyl-triclosan. For 2,4-Dichlorophenol and 2,4,6-Trichlorophenol, the best results were achieved with only a single sensor. Finally, working as multi-analyte quantification devices, the electronic tongues could provide information regarding the degradation kinetic and concentrations ranges in a dynamic removal treatment.
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Apostol, Ioana, Marius Preda, Constantin Nila, and Ion Bica. "IoT Botnet Anomaly Detection Using Unsupervised Deep Learning." Electronics 10, no. 16 (August 4, 2021): 1876. http://dx.doi.org/10.3390/electronics10161876.
Повний текст джерелаАнотація:
The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.
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Hayat, Ahatsham, Preety Baglat, Fábio Mendonça, Sheikh Shanawaz Mostafa, and Fernando Morgado-Dias. "Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images." International Journal of Environmental Research and Public Health 20, no. 2 (January 10, 2023): 1268. http://dx.doi.org/10.3390/ijerph20021268.
Повний текст джерелаАнотація:
The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people’s health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.
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Lwimbo, Komakech, and Muzuka. "Impacts of Emerging Agricultural Practices on Groundwater Quality in Kahe Catchment, Tanzania." Water 11, no. 11 (October 28, 2019): 2263. http://dx.doi.org/10.3390/w11112263.
Повний текст джерелаАнотація:
This paper assesses the impacts of farmers’ intensive use of agrochemicals (fertilizers and pesticides) on groundwater quality in the Kahe catchment. Samples were collected during the wet and dry seasons of the year 2018 and analyzed for the presence of agrochemicals in the water. Groundwater chemistry was dominated by magnesium-sodium-bicarbonate (Mg-Na-HCO3−). The cations levels were in the trend of Mg2+ >Na+ > Ca2+ > K+, whereas anions were HCO3− > Cl− > SO42− for both seasons. The NO3− had an average value of about 18.40 ± 4.04 and 7.6 ± 1.7 mg/L in the wet and dry season, respectively. Elevated levels of nitrate, sulfate, phosphate, and ammonium were found in water samples collected near the large-scale sugarcane plantation in the catchment. For both seasons, Pb, Cd, Fe, Mn, Zn and Cu concentrations averaged approximately 0.08 ± 0.03, 0.11 ± 0.03, 0.16 ± 0.02, 0.11 ± 0.01, 0.46 ± 0.05, and 0.55 ± 0.02 mg/L, respectively. On the other hand, the concentrations were higher in shallow wells than in the deep boreholes. Pesticides’ residues were below the detection limit in all sampled groundwater. The findings from this study provide important information for intervention in groundwater quality management in Kahe Catchment, Tanzania.
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Al lelah, Turki, George Theodorakopoulos, Philipp Reinecke, Amir Javed, and Eirini Anthi. "Abuse of Cloud-Based and Public Legitimate Services as Command-and-Control (C&C) Infrastructure: A Systematic Literature Review." Journal of Cybersecurity and Privacy 3, no. 3 (September 1, 2023): 558–90. http://dx.doi.org/10.3390/jcp3030027.
Повний текст джерелаАнотація:
The widespread adoption of cloud-based and public legitimate services (CPLS) has inadvertently opened up new avenues for cyber attackers to establish covert and resilient command-and-control (C&C) communication channels. This abuse poses a significant cybersecurity threat, as it allows malicious traffic to blend seamlessly with legitimate network activities. Traditional detection systems are proving inadequate in accurately identifying such abuses, emphasizing the urgent need for more advanced detection techniques. In our study, we conducted an extensive systematic literature review (SLR) encompassing the academic and industrial literature from 2008 to July 2023. Our review provides a comprehensive categorization of the attack techniques employed in CPLS abuses and offers a detailed overview of the currently developed detection strategies. Our findings indicate a substantial increase in cloud-based abuses, facilitated by various attack techniques. Despite this alarming trend, the focus on developing detection strategies remains limited, with only 7 out of 91 studies addressing this concern. Our research serves as a comprehensive review of CPLS abuse for the C&C infrastructure. By examining the emerging techniques used in these attacks, we aim to make a significant contribution to the development of effective botnet defense strategies.
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Tang, Ying, Xuming Lou, Zifeng Chen, and Chengjin Zhang. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing." Sustainability 12, no. 7 (March 27, 2020): 2655. http://dx.doi.org/10.3390/su12072655.
Повний текст джерелаАнотація:
Technology convergence has become a typical characteristic of innovation, which affects the evolution of industrial structures and the core competitiveness of organizations. However, the existing research has mainly focused on the development of core areas of convergence, ignoring the potential breakthroughs that emerging peripheral convergence may bring. Therefore, this research put forward a comprehensive methodology based on IPC (International Patent Classification) co-occurrence analysis to study the dynamic patterns of technology convergence from the perspectives of reinforcing convergence and novel convergence. For the former, convergence trends in each period were explored by using association rules, and the convergence degree was measured based on the number of patents containing different IPC codes. Then, the corresponding core technical fields were identified by using information entropy. For the latter, a community detection algorithm based on IPC co-occurrence network was adopted to investigate the convergence trend by period, and important technology fields were identified by the centrality indicators. The methodology proposed in this study is beneficial for firms to seize technological opportunities in technology convergence.
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Gul, Ijaz, Shiyao Zhai, Xiaoyun Zhong, Qun Chen, Xi Yuan, Zhicheng Du, Zhenglin Chen, et al. "Angiotensin-Converting Enzyme 2-Based Biosensing Modalities and Devices for Coronavirus Detection." Biosensors 12, no. 11 (November 7, 2022): 984. http://dx.doi.org/10.3390/bios12110984.
Повний текст джерелаАнотація:
Rapid and cost-effective diagnostic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a critical and valuable weapon for the coronavirus disease 2019 (COVID-19) pandemic response. SARS-CoV-2 invasion is primarily mediated by human angiotensin-converting enzyme 2 (hACE2). Recent developments in ACE2-based SARS-CoV-2 detection modalities accentuate the potential of this natural host-virus interaction for developing point-of-care (POC) COVID-19 diagnostic systems. Although research on harnessing ACE2 for SARS-CoV-2 detection is in its infancy, some interesting biosensing devices have been developed, showing the commercial viability of this intriguing new approach. The exquisite performance of the reported ACE2-based COVID-19 biosensors provides opportunities for researchers to develop rapid detection tools suitable for virus detection at points of entry, workplaces, or congregate scenarios in order to effectively implement pandemic control and management plans. However, to be considered as an emerging approach, the rationale for ACE2-based biosensing needs to be critically and comprehensively surveyed and discussed. Herein, we review the recent status of ACE2-based detection methods, the signal transduction principles in ACE2 biosensors and the development trend in the future. We discuss the challenges to development of ACE2-biosensors and delineate prospects for their use, along with recommended solutions and suggestions.
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Subramanian, Shyamala, Sashikala Mishra, Shruti Patil, Kailash Shaw, and Ebrahim Aghajari. "Machine Learning Styles for Diabetic Retinopathy Detection: A Review and Bibliometric Analysis." Big Data and Cognitive Computing 6, no. 4 (December 12, 2022): 154. http://dx.doi.org/10.3390/bdcc6040154.
Повний текст джерелаАнотація:
Diabetic retinopathy (DR) is a medical condition caused by diabetes. The development of retinopathy significantly depends on how long a person has had diabetes. Initially, there may be no symptoms or just a slight vision problem due to impairment of the retinal blood vessels. Later, it may lead to blindness. Recognizing the early clinical signs of DR is very important for intervening in and effectively treating DR. Thus, regular eye check-ups are necessary to direct the person to a doctor for a comprehensive ocular examination and treatment as soon as possible to avoid permanent vision loss. Nevertheless, due to limited resources, it is not feasible for screening. As a result, emerging technologies, such as artificial intelligence, for the automatic detection and classification of DR are alternative screening methodologies and thereby make the system cost-effective. People have been working on artificial-intelligence-based technologies to detect and analyze DR in recent years. This study aimed to investigate different machine learning styles that are chosen for diagnosing retinopathy. Thus, a bibliometric analysis was systematically done to discover different machine learning styles for detecting diabetic retinopathy. The data were exported from popular databases, namely, Web of Science (WoS) and Scopus. These data were analyzed using Biblioshiny and VOSviewer in terms of publications, top countries, sources, subject area, top authors, trend topics, co-occurrences, thematic evolution, factorial map, citation analysis, etc., which form the base for researchers to identify the research gaps in diabetic retinopathy detection and classification.
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Losurdo, Giuseppe, Floriana Giorgio, Maria Pricci, Bruna Girardi, Francesco Russo, Giuseppe Riezzo, Manuela Martulli, et al. "Helicobacter pylori Primary and Secondary Genotypic Resistance to Clarithromycin and Levofloxacin Detection in Stools: A 4-Year Scenario in Southern Italy." Antibiotics 9, no. 10 (October 21, 2020): 723. http://dx.doi.org/10.3390/antibiotics9100723.
Повний текст джерелаАнотація:
Antibiotic resistance has become an emerging problem for treating Helicobacter pylori (H. pylori) infection. Clarithromycin and levofloxacin are two key antibiotics used for its eradication. Therefore, we reviewed our experience with genotypic resistance analysis in stools to both clarithromycin and levofloxacin in the last four years to evaluate time trends, both in naive and failure patients. Patients collected a fecal sample using the THD fecal test device. Real-time polymerase chain reaction was performed to detect point mutations conferring resistance to clarithromycin (A2142C, A2142G, and A2143G in 23S rRNA) and levofloxacin (substitutions at amino acid position 87 and 91 of gyrA). One hundred and thirty-five naive patients were recruited between 2017–2020. Clarithromycin resistance was detected in 37 (27.4%). The time trend did not show any significant variation from 2017 to 2020 (p = 0.33). Primary levofloxacin resistance was found in 26 subjects (19.2%), and we observed a dramatic increase in rates from 2017 (10%) to 2018 (3.3%), 2019 (20%), and 2020 (37.8%). Ninety-one patients with at least one eradication failure were recruited. Secondary resistance to clarithromycin and levofloxacin was found in 59 (64.8%) and 45 patients (59.3%), respectively. In conclusion, our geographic area has a high risk of resistance to clarithromycin. There is also a progressive spreading of levofloxacin-resistant strains.
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Shults, Roman, Andriy Annenkov, Oleksandr Isaev, and Olena Nesterenko. "GIS Analysis of Temperature Variation in Ukraine for 2000-2020." Journal of Environmental Science and Engineering Technology 9 (October 18, 2021): 37–46. http://dx.doi.org/10.12974/2311-8741.2021.09.4.
Повний текст джерелаАнотація:
The presented paper is dedicated to the problem of temperature changes variations for Ukraine. As an input data source, NOAA National Centers for Environmental Information data were used. Based on preliminary studies, the temperature analysis workflow was developed. The feature of the deployed workflow is an application of GIS analysis for temperature trend detection. As the main functions of GIS analysis, overlay analysis and Kriging interpolation were used. The temperature surfaces were constructed using the Kriging interpolation method to determine the temperature trend. The overlay analysis was applied based on the temperature surfaces for different time intervals. After overlaying procedure, the difference between two georeferenced surfaces was calculated. These differences allowed us to track the temperature changes throughout time. The results of surfaces subtraction were used for temperature variation analysis for different time intervals. The general picture emerging from the research is that the temperature grows for 0.2 C° each five years. As in previous studies, the results confirm that there is a distinct tendency in the temperature rising for one degree of Celsius during the last twenty years. The temperature trend has an essential geographical association. The most significant temperature changes are related to the southeast of Ukraine, proving the desert regions' increasing.
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Kini, Anita S., Prema K. V. Reddy, and Smitha N. Pai. "Techniques of deep learning and image processing in plant leaf disease detection: a review." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (June 1, 2023): 3029. http://dx.doi.org/10.11591/ijece.v13i3.pp3029-3040.
Повний текст джерелаАнотація:
<span lang="EN-US">Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold <span>the ophthalmic functionality of the human eye. In agriculture, leaf examination</span> is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.</span>
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Wang, Qing Dong, and Jian Feng Wei. "Wireless Transmission System Design of Main Shaft Hoist Load Data." Key Engineering Materials 439-440 (June 2010): 417–21. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.417.
Повний текст джерелаАнотація:
Wireless data communication technology under industrial environment is a new development trend in recent years, applying wireless technology to the field of data collection can solve problems and shortcomings caused by wired network, such as cabling, faulty inspection difficulties and so on. Safe operation of mine hoist matters much to the safety of mine production. As a result of the lack of detection measures hoist overload occurs from time to time, it poses a greater threat to safety in production. Combing sensor technology with emerging wireless communication technology, the wireless data transmission technology program that takes P89LPC935 MCU as cored controlling component is designed and provided in this project, constructing a new type of real-time monitoring system for main shaft hoist load.
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Zhao, Yuan, Lu Zhang, Jiajun Deng, and Yanyong Zhang. "BEV-Radar: Bidirectional Radar-Camera Fusion for 3D Object Detection." JUSTC 53 (2023): 1. http://dx.doi.org/10.52396/justc-2023-0006.
Повний текст джерелаАнотація:
Exploring Millimeter Wave Radar data as complementary to RGB images for ameliorating 3D object detection has become an emerging trend for autonomous driving systems. However, existing Radar-camera fusion methods are highly dependent on the prior camera detection results, which renders the overall performance unsatisfactory. In this paper, we propose a bidirectional fusion scheme in the bird-eye view (BEV), which is independent of prior camera detection results. Leveraging features from both modalities, our method designs a bidirectional attention-based fusion strategy. Specifically, following BEV-based 3D detection methods, our method engages a bidirectional transformer to embed information from both modalities and enforces the local spatial relationship according to subsequent convolution blocks. After embedding the features, the BEV features are decoded in the 3D object prediction head. We evaluate our method on the nuScenes dataset, achieving 48.2 mAP and 57.6 NDS. The result shows considerable improvements compared to the camera-only baseline, especially in terms of velocity prediction. The code is available at <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/Etah0409/BEV-Radar">https://github.com/Etah0409/BEV-Radar</ext-link>}{<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/Etah0409/BEV-Radar">https://github.com/Etah0409/BEV-Radar</ext-link>.
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Gökdemir, Fatma Şeyma, Özlem Darcansoy İşeri, Abhishek Sharma, Premila N. Achar, and Füsun Eyidoğan. "Metagenomics Next Generation Sequencing (mNGS): An Exciting Tool for Early and Accurate Diagnostic of Fungal Pathogens in Plants." Journal of Fungi 8, no. 11 (November 13, 2022): 1195. http://dx.doi.org/10.3390/jof8111195.
Повний текст джерелаАнотація:
Crop output is directly impacted by infections, with fungi as the major plant pathogens, making accurate diagnosis of these threats crucial. Developing technology and multidisciplinary approaches are turning to genomic analyses in addition to traditional culture methods in diagnostics of fungal plant pathogens. The metagenomic next-generation sequencing (mNGS) method is preferred for genotyping identification of organisms, identification at the species level, illumination of metabolic pathways, and determination of microbiota. Moreover, the data obtained so far show that this new approach is promising as an emerging new trend in fungal disease detection. Another approach covered by mNGS technologies, known as metabarcoding, enables use of specific markers specific to a genetic region and allows for genotypic identification by facilitating the sequencing of certain regions. Although the core concept of mNGS remains constant across applications, the specific sequencing methods and bioinformatics tools used to analyze the data differ. In this review, we focus on how mNGS technology, including metabarcoding, is applied for detecting fungal pathogens and its promising developments for the future.
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Beissinger, Abby, and Debra Ann Inglis. "Greenhouse Comparison of Two Detection Methods for Potato virus YN-Wi at Four Potato Growth Stages." Plant Health Progress 19, no. 1 (January 1, 2018): 71–75. http://dx.doi.org/10.1094/php-11-17-0072-rs.
Повний текст джерелаАнотація:
Potato virus Y (PVY) causes significant crop and monetary losses. Owing to the prevalence of newly emerging strains of PVY such as PVYN-Wi, which often cause asymptomatic to mild reactions on certain potato cultivars, accurate tools are required to detect the virus in potato production. This study compared the sensitivity of a rapid field detection method (immunostrips) with a common laboratory detection method (triple antibody sandwich ELISA) on cultivar Chieftain, grown under isolated conditions in a greenhouse and mechanically inoculated with PVYN-Wi, at four potato growth stages (emergence, preflower, postflower, and senescence). Plants inoculated at emergence displayed severe symptoms of mosaic, veinal necrosis, and leaf drop. Plants inoculated at preflower, postflower, and senescence had veinal necrosis but low or no incidence of mosaic and leaf drop. Overall, few or no tuber symptoms were observed, but a trend of lower tuber yield occurred for emergence-inoculated plants. Low variability in PVYN-Wi detection occurred in both tests for emergence-inoculated plants, whereas those inoculated at preflower and postflower had more variability. Because symptom expression may differ depending on the growth stage when a plant becomes infected, these variations should be heeded with either detection method when collecting samples for PVY testing.
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Alonso-Montesinos, Joaquín. "Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera." Remote Sensing 12, no. 9 (April 27, 2020): 1382. http://dx.doi.org/10.3390/rs12091382.
Повний текст джерелаАнотація:
Characterizing the atmosphere is one of the most complex studies one can undertake due to the non-linearity and phenomenological variability. Clouds are also among the most variable atmospheric constituents, changing their size and shape over a short period of time. There are several sectors in which the study of cloudiness is of vital importance. In the renewable field, the increasing development of solar technology and the emerging trend for constructing and operating solar plants across the earth’s surface requires very precise control systems that provide optimal energy production management. Similarly, airports are hubs where cloud coverage is required to provide high-precision periodic observations that inform airport operators about the state of the atmosphere. This work presents an autonomous cloud detection system, in real time, based on the digital image processing of a low-cost sky camera. An algorithm was developed to identify the clouds in the whole image using the relationships established between the channels of the RGB and Hue, Saturation, Value (HSV) color spaces. The system’s overall success rate is approximately 94% for all types of sky conditions; this is a novel development which makes it possible to identify clouds from a ground perspective without the use of radiometric parameters.
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Neujahr, Alison, Duan Loy, J. Dustin Loy, Bruce Brodersen, and Samodha Fernando. "66 Utilizing Third Generation Sequencing to Identify Pathogen Outbreaks Rapidly and Accurately." Journal of Animal Science 100, Supplement_2 (April 12, 2022): 28. http://dx.doi.org/10.1093/jas/skac064.045.
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Abstract The temporal trend of novel pathogen identification has increased over the last decade, with viruses being the main contributor to the increase. Currently, the NAHLN provides veterinary surveillance and testing procedures for high consequence animal diseases, where in many cases, the detection of these diseases is based on real-time PCR assay (ASF, CSF, FMD, and IAV-S) or pathogen specific antibodies using ELISA methods (PRV). However, genetic shift and drift in virus genomes can lead to failure in detection of novel or emerging pathogens. Therefore, with the current trend of increased viral disease emergence, novel methods are needed to accurately and rapidly identify and characterize viral pathogens independent of knowing genomic information. In this study, we developed a novel diagnostic approach to identifying swine pathogens, using surrogate viruses. We implemented the use of Oxford Nanopore MinION Technology to detect long read sequences in real time. Surrogate viruses were used in place of common pathogens such as African Swine Fever, Classical Swine Fever, Pseudorabies, Foot and Mouth Disease, and Influenza A to investigate rapid detection limits. To increase the complexity of a sample, both DNA and RNA viruses were spiked into tissues were analyzed using Oxford Nanopore MinION Technology. This method can detect both DNA and RNA viruses simultaneously without prior knowledge. Additionally, as sequences can be detected real-time, we were able to confidently detect viral pathogens within a sample within 30 min. Additionally, we have established a library preparation and sequencing protocol as well as an informatic pipeline that could allow clinics and diagnostics centers to identify pathogens in less than 10 hours.
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Natu, Rucha, Luke Herbertson, Grazziela Sena, Kate Strachan, and Suvajyoti Guha. "A Systematic Analysis of Recent Technology Trends of Microfluidic Medical Devices in the United States." Micromachines 14, no. 7 (June 24, 2023): 1293. http://dx.doi.org/10.3390/mi14071293.
Повний текст джерелаАнотація:
In recent years, the U.S. Food and Drug Administration (FDA) has seen an increase in microfluidic medical device submissions, likely stemming from recent advancements in microfluidic technologies. This recent trend has only been enhanced during the COVID-19 pandemic, as microfluidic-based test kits have been used for diagnosis. To better understand the implications of this emerging technology, device submissions to the FDA from 2015 to 2021 containing microfluidic technologies have been systematically reviewed to identify trends in microfluidic medical applications, performance tests, standards used, fabrication techniques, materials, and flow systems. More than 80% of devices with microfluidic platforms were found to be diagnostic in nature, with lateral flow systems accounting for about 35% of all identified microfluidic devices. A targeted analysis of over 40,000 adverse event reports linked to microfluidic technologies revealed that flow, operation, and data output related failures are the most common failure modes for these device types. Lastly, this paper highlights key considerations for developing new protocols for various microfluidic applications that use certain analytes (e.g., blood, urine, nasal-pharyngeal swab), materials, flow, and detection mechanisms. We anticipate that these considerations would help facilitate innovation in microfluidic-based medical devices.
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Manchanda, Vikas. "Trend of Antimicrobial Susceptibility Profile of Vibrio cholera Strains Isolated in Indian Children’s during 2008-2016." Journal of Communicable Diseases 53, no. 01 (March 31, 2021): 67–71. http://dx.doi.org/10.24321/0019.5138.202111.
Повний текст джерелаАнотація:
Background: The unique epidemiologic attribute of the cholera is its propensity to occur as an outbreak that may flare- up into epidemics, if not controlled. The causative bacterial pathogen Vibrio cholerae prevails in the environment and infects humans whenever there is a breakdown in the public health component. The upsurge in antimicrobial resistance directly influences the management and spread of the disease. Objectives: The present study documents the epidemiological profile and the changing trends in antimicrobial resistance of eight years (2008-2016) in Vibrio cholerae isolates. Methodology: A retrospective study was undertaken with review of records of a period of eight years (January 2008 to December 2016) from two government hospitals in Delhi. All data were captured in WHONET and was analyzed. V. cholerae isolates were identified using standard microbiological techniques and were serotype using antisera. Antimicrobial susceptibility testing was performed using disc diffusion and Vitek-2 automated method. Result: During the period of eight years, 315 cases were confirmed microbiologically as cholera. A significant outbreak of cholera (88 cases) occurred in 2013 followed by sporadic cases in 2012 through 2016. Males outnumbered the females by Male to female ratio of 1:0.8. Mostly cases presented during the months of June to October. Almost all (92%) isolates were V. cholerae O1, biotype ElToR and serotype Ogawa. The antibiogram over the period of eight years showed that isolates were consistently sensitive to aminoglycosides. However, emerging resistance was seen to quinolones and β-Lactam group. Conclusion: The emergence of resistance amongst V. cholerae especially towards quinolones may significantly influence the control strategies in future outbreaks. Therefore, continuous surveillance with regards to drug resistance, as well as epidemiological variation is necessary for early detection. A strong regional commitment may help contain the disease.
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Pourezzat, Ali Asghar, Mohammad Hoseini Moghadam, Maryam Sani Ejlal, and Ghazaleh Taheriattar. "The future of governance in Iran." foresight 20, no. 2 (April 9, 2018): 175–89. http://dx.doi.org/10.1108/fs-10-2017-0056.
Повний текст джерелаАнотація:
Purpose Through an examination of macro-historical studies on the governance of Iran, the purpose of this study is to identify the most significant and important events and trends in the rise and fall of Iranian governments and introduce alternative futures in a range of possible, plausible and preferable forms of future governance. To carry out a foresight study of alternative futures of Iranian governance, the authors used futures studies, based on the detection of the most critical driving forces, which are also the most important uncertainties. Futures studies as an interdisciplinary field of study help to identify the events and trends that affect political change and offer scenarios of four alternative futures for the governance of Iran: Smart and Stable Government, Authoritarian Development-oriented Government, Irrational Government and Irrational Breakable Government. The authors believe that Iran’s endeavors to promote democracy, taking the changing international trends into account, make a more trustworthy future for Iran both possible plausible. Design/methodology/approach Based on macro-history approach and by using “shared history”, future triangle and then scenario planning, the future of Governance in Iran has been analyzed. Findings Whenever the government has distanced itself from the public and has neglected the trend of international change, it has been faced with a period of collapse and annihilation. And whenever these two important factors are understood, the result is a trend of development and growth. Therefore, the most favored image of Iran’s future relies on the maintenance and promotion of public participation and on increasing attention to the sustainable realities of international relations. Originality/value The complexities of events and trends affecting the rise and fall of previous governments of Iran make it necessary to use an interdisciplinary approach to understand the events that have emerged or are emerging in its governance. In this study, from futures studies point of view, transformation of governance has been studied.
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Mumtaz, Zilwa, Zubia Rashid, Ashaq Ali, Afsheen Arif, Fuad Ameen, Mona S. AlTami, and Muhammad Zubair Yousaf. "Prospects of Microfluidic Technology in Nucleic Acid Detection Approaches." Biosensors 13, no. 6 (May 27, 2023): 584. http://dx.doi.org/10.3390/bios13060584.
Повний текст джерелаАнотація:
Conventional diagnostic techniques are based on the utilization of analyte sampling, sensing and signaling on separate platforms for detection purposes, which must be integrated to a single step procedure in point of care (POC) testing devices. Due to the expeditious nature of microfluidic platforms, the trend has been shifted toward the implementation of these systems for the detection of analytes in biochemical, clinical and food technology. Microfluidic systems molded with substances such as polymers or glass offer the specific and sensitive detection of infectious and noninfectious diseases by providing innumerable benefits, including less cost, good biological affinity, strong capillary action and simple process of fabrication. In the case of nanosensors for nucleic acid detection, some challenges need to be addressed, such as cellular lysis, isolation and amplification of nucleic acid before its detection. To avoid the utilization of laborious steps for executing these processes, advances have been deployed in this perspective for on-chip sample preparation, amplification and detection by the introduction of an emerging field of modular microfluidics that has multiple advantages over integrated microfluidics. This review emphasizes the significance of microfluidic technology for the nucleic acid detection of infectious and non-infectious diseases. The implementation of isothermal amplification in conjunction with the lateral flow assay greatly increases the binding efficiency of nanoparticles and biomolecules and improves the limit of detection and sensitivity. Most importantly, the deployment of paper-based material made of cellulose reduces the overall cost. Microfluidic technology in nucleic acid testing has been discussed by explicating its applications in different fields. Next-generation diagnostic methods can be improved by using CRISPR/Cas technology in microfluidic systems. This review concludes with the comparison and future prospects of various microfluidic systems, detection methods and plasma separation techniques used in microfluidic devices.
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Tseng, Ryan, Ching-Chuen Chen, Sheng-Min Hsu, and Han-Sheng Chuang. "Contact-Lens Biosensors." Sensors 18, no. 8 (August 13, 2018): 2651. http://dx.doi.org/10.3390/s18082651.
Повний текст джерелаАнотація:
Rapid diagnosis and screening of diseases have become increasingly important in predictive and preventive medicine as they improve patient treatment strategies and reduce cost as well as burden on our healthcare system. In this regard, wearable devices are emerging as effective and reliable point-of-care diagnostics that can allow users to monitor their health at home. These wrist-worn, head-mounted, smart-textile, or smart-patches devices can offer valuable information on the conditions of patients as a non-invasive form of monitoring. However, they are significantly limited in monitoring physiological signals and biomechanics, and, mostly, rely on the physical attributes. Recently, developed wearable devices utilize body fluids, such as sweat, saliva, or skin interstitial fluid, and electrochemical interactions to allow continuous physiological condition and disease monitoring for users. Among them, tear fluid has been widely utilized in the investigation of ocular diseases, diabetes, and even cancers, because of its easy accessibility, lower complexity, and minimal invasiveness. By determining the concentration change of analytes within the tear fluid, it would be possible to identify disease progression and allow patient-oriented therapies. Considering the emerging trend of tear-based biosensing technology, this review article aims to focus on an overview of the tear fluid as a detection medium for certain diseases, such as ocular disorders, diabetes, and cancer. In addition, the rise and application of minimally invasive detection and monitoring via integrated contact lens biosensors will also be addressed, in regards to their practicality and current developmental progress.
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Bora, Mousumi, Manu M, Dayamon D. Mathew, Himasri Das, Durlav Prasad Bora, and Nagendra Nath Barman. "Point of care diagnostics and non-invasive sampling strategy: a review on major advances in veterinary diagnostics." Acta Veterinaria Brno 91, no. 1 (2022): 17–34. http://dx.doi.org/10.2754/avb202291010017.
Повний текст джерелаАнотація:
The use of point of care diagnostics (POCD) in animal diseases has steadily increased over the years since its introduction. Its potential application to diagnose infectious diseases in remote and resource limited settings have made it an ideal diagnostic in animal disease diagnosis and surveillance. The rapid increase in incidence of emerging infectious diseases requires urgent attention where POCD could be indispensable tools for immediate detection and early warning of a potential pathogen. The advantages of being rapid, easily affordable and the ability to diagnose an infectious disease on spot has driven an intense effort to refine and build on the existing technologies to generate advanced POCD with incremental improvements in analytical performance to diagnose a broad spectrum of animal diseases. The rural communities in developing countries are invariably affected by the burden of infectious animal diseases due to limited access to diagnostics and animal health personnel. Besides, the alarming trend of emerging and transboundary diseases with pathogen spill-overs at livestock-wildlife interfaces has been identified as a threat to the domestic population and wildlife conservation. Under such circumstances, POCD coupled with non-invasive sampling techniques could be successfully deployed at field level without the use of sophisticated laboratory infrastructures. This review illustrates the current and prospective POCD for existing and emerging animal diseases, the status of non-invasive sampling strategies for animal diseases, and the tremendous potential of POCD to uplift the status of global animal health care.
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Hsu, Wei-Yen. "A customer-oriented skin detection and care system in telemedicine applications." Electronic Library 37, no. 6 (December 9, 2019): 1007–21. http://dx.doi.org/10.1108/el-03-2019-0080.
Повний текст джерелаАнотація:
Purposed Virtual medical instrumentation plays a vital role in a telemedicine system that obtains data from the medical instrument, required by doctors at remote location to diagnose a patient. In recent years, the analysis of skin quality by telemedicine system has become an emerging trend. To allow the skin to complement the beauty products and achieve better improvement results, the purpose of this study is to provide advice on a system that can objectively evaluate the condition of the skin of the face and to match appropriate beauty and cosmetic products. Design/methodology/approach A novel customer-oriented medical system is proposed for the applications of telemedicine in this study, whose aim is to improve information transfer quality and rate to further enhance the communication between medical staffs and patients in the telemedicine. More specifically, facial skin will be recorded with digital images, and skin detection will be performed using image processing technology to facilitate doctors to provide medical treatment for the patients at far end. Findings The roughness, freckles and acne indicators were evaluated after obtaining skin images. These three indicators were used as input to the system, and skin scores were then calculated to evaluate skin conditions to further provide more matching skin care. Originality/value This can improve the health problems that have occurred and can also record the skin condition for each test. Experimental results suggest that it is suitable for the applications of telemedicine.
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Caja, Gerardo, Andreia Castro-Costa, and Christopher H. Knight. "Engineering to support wellbeing of dairy animals." Journal of Dairy Research 83, no. 2 (May 2016): 136–47. http://dx.doi.org/10.1017/s0022029916000261.
Повний текст джерелаАнотація:
Current trends in the global milk market and the recent abolition of milk quotas have accelerated the trend of the European dairy industry towards larger farm sizes and higher-yielding animals. Dairy cows remain in focus, but there is a growing interest in other dairy species, whose milk is often directed to traditional and protected designation of origin and gourmet dairy products. The challenge for dairy farms in general is to achieve the best possible standards of animal health and welfare, together with high lactational performance and minimal environmental impact. For larger farms, this may need to be done with a much lower ratio of husbandry staff to animals. Recent engineering advances and the decreasing cost of electronic technologies has allowed the development of ‘sensing solutions’ that automatically collect data, such as physiological parameters, production measures and behavioural traits. Such data can potentially help the decision making process, enabling early detection of health or wellbeing problems in individual animals and hence the application of appropriate corrective husbandry practices. This review focuses on new knowledge and emerging developments in welfare biomarkers (e.g. stress and metabolic diseases), activity-based welfare assessment (e.g. oestrus and lameness detection) and sensors of temperature and pH (e.g. calving alert and rumen function) and their combination and integration into ‘smart’ husbandry support systems that will ensure optimum wellbeing for dairy animals and thereby maximise farm profitability. Use of novel sensors combined with new technologies for information handling and communication are expected to produce dramatic changes in traditional dairy farming systems.
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Calzolari, Mattia, Elena Carra, Gianluca Rugna, Paolo Bonilauri, Federica Bergamini, Romeo Bellini, Stefania Varani, and Michele Dottori. "Isolation and Molecular Typing of Leishmania infantum from Phlebotomus perfiliewi in a Re-Emerging Focus of Leishmaniasis, Northeastern Italy." Microorganisms 7, no. 12 (December 3, 2019): 644. http://dx.doi.org/10.3390/microorganisms7120644.
Повний текст джерелаАнотація:
Visceral leishmaniasis (VL) caused by Leishmania (L.) infantum is a public health threat in the Emilia-Romagna region, northeastern Italy, but its epidemiology has not been fully elucidated in this area. The objective of this study was to characterize Leishmania infection in sand flies collected in a re-emerging focus of VL in the Bologna province. During the summer of 2016, 6114 sand flies were collected, identified, and tested for Leishmania detection. Of the identified sand flies, 96.5% were Phlebotomus (P.) perfiliewi and 3.5% were P. perniciosus. Detected parasites were characterized by biomolecular methods (multilocus microsatellite typing and characterization of repetitive region on chromosome 31), and quantified by real-time PCR. The prevalence of Leishmania infection in individually-tested P. perfiliewi sand flies varied from 6% to 10% with an increasing trend during the season. Promastigotes of L. infantum were isolated by dissection in one P. perfiliewi female; the isolated strain (Lein-pw) were closely related to Leishmania parasites from VL cases in northeastern Italy, but differed from strains isolated in dogs from the same area. Our findings strongly support the vector status of P. perfiliewi for human VL in the study area.