ResearchPad - mathematics https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Estimating the potential impact of behavioral public health interventions nationally while maintaining agreement with global patterns on relative risks]]> https://www.researchpad.co/article/elastic_article_13880 This paper introduces a novel method to evaluate the local impact of behavioral scenarios on disease prevalence and burden with representative individual level data while ensuring that the model is in agreement with the qualitative patterns of global relative risk (RR) estimates. The method is used to estimate the impact of behavioral scenarios on the burden of disease due to ischemic heart disease (IHD) and diabetes in the Turkish adult population.MethodsDisease specific Hierarchical Bayes (HB) models estimate the individual disease probability as a function of behaviors, demographics, socio-economics and other controls, where constraints are specified based on the global RR estimates. The simulator combines the counterfactual disease probability estimates with disability adjusted life year (DALY)-per-prevalent-case estimates and rolls up to the targeted population level, thus reflecting the local joint distribution of exposures. The Global Burden of Disease (GBD) 2016 study meta-analysis results guide the analysis of the Turkish National Health Surveys (2008 to 2016) that contain more than 90 thousand observations.FindingsThe proposed Qualitative Informative HB models do not sacrifice predictive accuracy versus benchmarks (logistic regression and HB models with non-informative and numerical informative priors) while agreeing with the global patterns. In the Turkish adult population, Increasing Physical Activity reduces the DALYs substantially for both IHD by 8.6% (6.4% 11.2%), and Diabetes by 8.1% (5.8% 10.6%), (90% uncertainty intervals). Eliminating Smoking and Second-hand Smoke predominantly decreases the IHD burden 13.1% (10.4% 15.8%) versus Diabetes 2.8% (1.1% 4.6%). Increasing Fruit and Vegetable Consumption, on the other hand, reduces IHD DALYs by 4.1% (2.8% 5.4%) while not improving the Diabetes burden 0.1% (0% 0.1%).ConclusionWhile the national RR estimates are in qualitative agreement with the global patterns, the scenario impact estimates are markedly different than the attributable risk estimates from the GBD analysis and allow evaluation of practical scenarios with multiple behaviors. ]]> <![CDATA[The two types of society: Computationally revealing recurrent social formations and their evolutionary trajectories]]> https://www.researchpad.co/article/elastic_article_13873 Comparative social science has a long history of attempts to classify societies and cultures in terms of shared characteristics. However, only recently has it become feasible to conduct quantitative analysis of large historical datasets to mathematically approach the study of social complexity and classify shared societal characteristics. Such methods have the potential to identify recurrent social formations in human societies and contribute to social evolutionary theory. However, in order to achieve this potential, repeated studies are needed to assess the robustness of results to changing methods and data sets. Using an improved derivative of the Seshat: Global History Databank, we perform a clustering analysis of 271 past societies from sampling points across the globe to study plausible categorizations inherent in the data. Analysis indicates that the best fit to Seshat data is five subclusters existing as part of two clearly delineated superclusters (that is, two broad “types” of society in terms of social-ecological configuration). Our results add weight to the idea that human societies form recurrent social formations by replicating previous studies with different methods and data. Our results also contribute nuance to previously established measures of social complexity, illustrate diverse trajectories of change, and shed further light on the finite bounds of human social diversity.

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<![CDATA[<i>In silico</i> analyses identify lncRNAs: WDFY3-AS2, BDNF-AS and AFAP1-AS1 as potential prognostic factors for patients with triple-negative breast tumors]]> https://www.researchpad.co/article/elastic_article_13870 Long non-coding RNAs (lncRNAs) are characterized as having 200 nucleotides or more and not coding any protein, and several been identified as differentially expressed in several human malignancies, including breast cancer.MethodsHere, we evaluated lncRNAs differentially expressed in triple-negative breast cancer (TNBC) from a cDNA microarray data set obtained in a previous study from our group. Using in silico analyses in combination with a review of the current literature, we identify three lncRNAs as potential prognostic factors for TNBC patients.ResultsWe found that the expression of WDFY3-AS2, BDNF-AS, and AFAP1-AS1 was associated with poor survival in patients with TNBCs. WDFY3-AS2 and BDNF-AS are lncRNAs known to play an important role in tumor suppression of different types of cancer, while AFAP1-AS1 exerts oncogenic activity.ConclusionOur findings provided evidence that WDFY3-AS2, BDNF-AS, and AFAP1-AS1 may be potential prognostic factors in TNBC development. ]]> <![CDATA[Low LEF1 expression is a biomarker of early T-cell precursor, an aggressive subtype of T-cell lymphoblastic leukemia]]> https://www.researchpad.co/article/elastic_article_13868 Early T-cell precursor (ETP) is the only subtype of acute T-cell lymphoblastic leukemia (T-ALL) listed in the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia. Patients with ETP tend to have worse disease outcomes. ETP is defined by a series of immune markers. The diagnosis of ETP status can be vague due to the limitation of the current measurement. In this study, we performed unsupervised clustering and supervised prediction to investigate whether a molecular biomarker can be used to identify the ETP status in order to stratify risk groups. We found that the ETP status can be predicted by the expression level of Lymphoid enhancer binding factor 1 (LEF1) with high accuracy (AUC of ROC = 0.957 and 0.933 in two T-ALL cohorts). The patients with ETP subtype have a lower level of LEF1 comparing to the those without ETP. We suggest that incorporating the biomarker LEF1 with traditional immune-phenotyping will improve the diagnosis of ETP.

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<![CDATA[Crystal structure of <i>Thermus thermophilus</i> methylenetetrahydrofolate dehydrogenase and determinants of thermostability]]> https://www.researchpad.co/article/elastic_article_13865 The elucidation of mechanisms behind the thermostability of proteins is extremely important both from the theoretical and applied perspective. Here we report the crystal structure of methylenetetrahydrofolate dehydrogenase (MTHFD) from Thermus thermophilus HB8, a thermophilic model organism. Molecular dynamics trajectory analysis of this protein at different temperatures (303 K, 333 K and 363 K) was compared with homologous proteins from the less temperature resistant organism Thermoplasma acidophilum and the mesophilic organism Acinetobacter baumannii using several data reduction techniques like principal component analysis (PCA), residue interaction network (RIN) analysis and rotamer analysis. These methods enabled the determination of important residues for the thermostability of this enzyme. The description of rotamer distributions by Gini coefficients and Kullback–Leibler (KL) divergence both revealed significant correlations with temperature. The emerging view seems to indicate that a static salt bridge/charged residue network plays a fundamental role in the temperature resistance of Thermus thermophilus MTHFD by enhancing both electrostatic interactions and entropic energy dispersion. Furthermore, this analysis uncovered a relationship between residue mutations and evolutionary pressure acting on thermophilic organisms and thus could be of use for the design of future thermostable enzymes.

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<![CDATA[Fruit and vegetable consumption in Europe according to gender, educational attainment and regional affiliation—A cross-sectional study in 21 European countries]]> https://www.researchpad.co/article/elastic_article_13846 The purpose of the present study was to examine fruit and vegetable consumption according to gender, educational attainment and regional affiliation in Europe.DesignCross-sectional study.Setting21 European countries.Participants37 672 adults participating in the 7th round of the European Social Survey.Main outcome measuresFruit and vegetable consumption was measured using two single frequency questions. Responses were dichotomized into low (<once a day) and high (≥once a day) consumption. The association between consumption of fruit and vegetables and gender, educational level, regional affiliation was examined using logistic regression analyses.ResultsOverall, females showed increased odds of consuming fruit (OR 1.71 (95%CI:1.62, 1.79) and vegetable (1.59 (1.51, 1.67)) compared to males and high educated participants showed increased odds of consuming fruit (1.53 (1.43, 1.63)) and vegetables (1.86 (1.74, 2.00)) compared to low educated participants. Our results also showed that participants living in Eastern Europe had the lowest odds of consuming fruit and vegetables, whereas participants from Southern- and Northern Europe had the highest odds of consuming fruit and vegetables, respectively. Results from interaction analyses confirmed the positive association between fruit and vegetable consumption and educational level, although for some European regions, decreased odds of fruit and vegetables was observed among medium educated participants compared to those with low education.ConclusionsOverall, the present study showed that being female and having a high education were associated with increased consumption of fruit and vegetables. However, the direction and strength of these relationships depends on regional affiliations. ]]> <![CDATA[Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis]]> https://www.researchpad.co/article/elastic_article_13837 In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar.

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<![CDATA[Mesh smoothing algorithm based on exterior angles split]]> https://www.researchpad.co/article/elastic_article_13823 Since meshes of poor quality give rise to low accuracy in finite element analysis and kinds of inconveniences in many other applications, mesh smoothing is widely used as an essential technique for the improvement of mesh quality. With respect to this issue, the main contribution of this paper is that a novel mesh smoothing method based on an exterior-angle-split process is proposed. The proposed method contains three main stages: the first stage is independent element geometric transformation performed by exterior-angle-split operations, treating elements unconnected; the second stage is to offset scaling and displacement induced by element transformation; the third stage is to determine the final positions of nodes with a weighted strategy. Theoretical proof describes the regularity of this method and many numerical experiments illustrate its convergence. Not only is this method applicable for triangular mesh, but also can be naturally extended to arbitrary polygonal surface mesh. Quality improvements of demonstrations on triangular and quadrilateral meshes show the effectiveness of this method.

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<![CDATA[Relationship between maximal incremental and high-intensity interval exercise performance in elite athletes]]> https://www.researchpad.co/article/elastic_article_13822 This descriptive study aimed to explore the physiological factors that determine tolerance to exertion during high-intensity interval effort. Forty-seven young women (15–28 years old) were enrolled: 23 athletes from Taiwan national or national reserve teams and 24 moderately active females. Each participant underwent a maximal incremental INC (modified Bruce protocol) cardiopulmonary exercise test on the first day and high-intensity interval testing (HIIT) on the second day, both performed on a treadmill. The HIIT protocol involved alternation between 1-min effort at 120% of the maximal speed, at the same slope reached at the end of the INC, and 1-min rest until volitional exhaustion. Gas exchange, heart rate (HR), and muscle oxygenation at the right vastus lateralis, measured by near-infrared spectroscopy, were continuously recorded. The number of repetitions completed (Rlim) by each participant was considered the HIIT tolerance index. The results showed a large difference in the Rlim (range, 2.6–12.0 repetitions) among the participants. Stepwise linear regression revealed that the variance in the Rlim within the cohort was related to the recovery rates of oxygen consumption (V˙O2), HR at the second minute after INC, and muscle tissue saturation index at exhaustion (R = 0.644). In addition, age was linearly correlated with Rlim (adjusted R = −0.518, p < 0.0001). In conclusion, the recovery rates for V˙O2 and HR after the incremental test, and muscle saturation index at exhaustion, were the major physiological factors related to HIIT performance. These findings provide insights into the role of the recovery phase after maximal INC exercise testing. Future research investigating a combination of INC and HIIT testing to determine training-induced performance improvement is warranted.

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<![CDATA[Mechanical characterization of PVA hydrogels’ rate-dependent response using multi-axial loading]]> https://www.researchpad.co/article/elastic_article_13820 The time-dependent properties of rubber-like synthesized and biological materials are crucial for their applications. Currently, this behavior is mainly measured using axial tensile test, compression test, or indentation. Limited studies performed on using multi-axial loading measurements of time-dependent material behavior exist in the literature. Therefore, the aim of this study is to investigate the viscoelastic response of rubber-like materials under multi-axial loading using cavity expansion and relaxation tests. The tests were performed on PVA hydrogel specimens. Three hyperelasitc models and one term Prony series were used to characterize the viscoelastic response of the hydrogels. Finite element (FE) simulations were performed to verify the validity of the calibrated material coefficients by reproducing the experimental results. The excellent agreement between the experimental, analytical and numerical data proves the capability of the cavity expansion technique to measure the time-dependent behavior of viscoelastic materials.

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<![CDATA[The early experiences of Physician Associate students in the UK: A regional cross-sectional study investigating factors associated with engagement]]> https://www.researchpad.co/article/elastic_article_13815 The number of physician associates (PAs) training and working in the UK has increased over the last few years following the proliferation of postgraduate courses. Understanding early experiences and what impacts on engagement is important if we are to appropriately support this relatively new professional group.MethodsThis paper reports on a cross-sectional analysis of the first year of data from a prospective 10-year longitudinal cohort study. First year PA students (n = 89) were enrolled from five universities in one UK region where the training programmes were less than 2 years old. Data collected were: demographic information, wellbeing, burnout and engagement, expectations, placement experience, performance and caring responsibilities. Pearson’s correlations were used to examine relationships between variables and to select variables for a hierarchical regression analysis to understand which factors were associated with engagement. Descriptive statistics were calculated for questions relating to experience.ResultsThe experiences of PA students during their first 3–6 months were mixed. For example, 78.7% of students felt that there were staff on placement they could go to for support, however, 44.8% reported that staff did not know about the role and 61.3% reported that staff did not know what clinical work they should undertake. Regression analysis found that their level of engagement was associated with their perceived career satisfaction, overall well-being, and caring responsibilities.ConclusionsThe support systems required for PAs may need to be examined as results showed that the PA student demographic is different to that of medical students and caring responsibilities are highly associated with engagement. A lack of understanding around the PA role in clinical settings may also need to be addressed in order to better support and develop this workforce. ]]> <![CDATA[Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019]]> https://www.researchpad.co/article/elastic_article_13811 The identification of statistical models for the accurate forecast and timely determination of the outbreak of infectious diseases is very important for the healthcare system. Thus, this study was conducted to assess and compare the performance of four machine-learning methods in modeling and forecasting brucellosis time series data based on climatic parameters.MethodsIn this cohort study, human brucellosis cases and climatic parameters were analyzed on a monthly basis for the Qazvin province–located in northwestern Iran- over a period of 9 years (2010–2018). The data were classified into two subsets of education (80%) and testing (20%). Artificial neural network methods (radial basis function and multilayer perceptron), support vector machine and random forest were fitted to each set. Performance analysis of the models were done using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Root Error (MARE), and R2 criteria.ResultsThe incidence rate of the brucellosis in Qazvin province was 27.43 per 100,000 during 2010–2019. Based on our results, the values of the RMSE (0.22), MAE (0.175), MARE (0.007) criteria were smaller for the multilayer perceptron neural network than their values in the other three models. Moreover, the R2 (0.99) value was bigger in this model. Therefore, the multilayer perceptron neural network exhibited better performance in forecasting the studied data. The average wind speed and mean temperature were the most effective climatic parameters in the incidence of this disease.ConclusionsThe multilayer perceptron neural network can be used as an effective method in detecting the behavioral trend of brucellosis over time. Nevertheless, further studies focusing on the application and comparison of these methods are needed to detect the most appropriate forecast method for this disease. ]]> <![CDATA[Operational method of reliability and content-validity analysis: Taking “trait-symptoms” screening of individuals at high-risk for OCD as an example]]> https://www.researchpad.co/article/elastic_article_13806 A well-designed self-reported scale is highly applicable to current clinical and research practices. However, the problems with the scale method, such as quantitative analysis of content validity and test-retest reliability analysis of state-like variables are yet to be resolved. The main purpose of this paper is to propose an operational method for solving these problems. Additionally, it aims to enhance understanding of the research paradigm for the scale method (excluding criterion-related validity). This paper used a study that involved screening of high-risk groups for OCD (Obsessive-Compulsive Disorder), conducted 5 rounds of tests, and developed scales, reliability, and validity analysis (using sample sizes of 496, 610, 600, 600 and 990). The operational method we propose is practical, feasible, and can be used to develop and validate a scale.

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<![CDATA[A model for the assessment of bluetongue virus serotype 1 persistence in Spain]]> https://www.researchpad.co/article/elastic_article_11225 Bluetongue virus (BTV) is an arbovirus of ruminants that has been circulating in Europe continuously for more than two decades and has become endemic in some countries such as Spain. Spain is ideal for BTV epidemiological studies since BTV outbreaks from different sources and serotypes have occurred continuously there since 2000; BTV-1 has been reported there from 2007 to 2017. Here we develop a model for BTV-1 endemic scenario to estimate the risk of an area becoming endemic, as well as to identify the most influential factors for BTV-1 persistence. We created abundance maps at 1-km2 spatial resolution for the main vectors in Spain, Culicoides imicola and Obsoletus and Pulicaris complexes, by combining environmental satellite data with occurrence models and a random forest machine learning algorithm. The endemic model included vector abundance and host-related variables (farm density). The three most relevant variables in the endemic model were the abundance of C. imicola and Obsoletus complex and density of goat farms (AUC 0.86); this model suggests that BTV-1 is more likely to become endemic in central and southwestern regions of Spain. It only requires host- and vector-related variables to identify areas at greater risk of becoming endemic for bluetongue. Our results highlight the importance of suitable Culicoides spp. prediction maps for bluetongue epidemiological studies and decision-making about control and eradication measures.

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<![CDATA[The Language of Innovation]]> https://www.researchpad.co/article/elastic_article_10245 Predicting innovation is a peculiar problem in data science. Following its definition, an innovation is always a never-seen-before event, leaving no room for traditional supervised learning approaches. Here we propose a strategy to address the problem in the context of innovative patents, by defining innovations as never-seen-before associations of technologies and exploiting self-supervised learning techniques. We think of technological codes present in patents as a vocabulary and the whole technological corpus as written in a specific, evolving language. We leverage such structure with techniques borrowed from Natural Language Processing by embedding technologies in a high dimensional euclidean space where relative positions are representative of learned semantics. Proximity in this space is an effective predictor of specific innovation events, that outperforms a wide range of standard link-prediction metrics. The success of patented innovations follows a complex dynamics characterized by different patterns which we analyze in details with specific examples. The methods proposed in this paper provide a completely new way of understanding and forecasting innovation, by tackling it from a revealing perspective and opening interesting scenarios for a number of applications and further analytic approaches.

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<![CDATA[Using case-level context to classify cancer pathology reports]]> https://www.researchpad.co/article/elastic_article_7869 Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence—for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks—site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

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<![CDATA[Pooling individual participant data from randomized controlled trials: Exploring potential loss of information]]> https://www.researchpad.co/article/elastic_article_7838 Pooling individual participant data to enable pooled analyses is often complicated by diversity in variables across available datasets. Therefore, recoding original variables is often necessary to build a pooled dataset. We aimed to quantify how much information is lost in this process and to what extent this jeopardizes validity of analyses results.MethodsData were derived from a platform that was developed to pool data from three randomized controlled trials on the effect of treatment of cardiovascular risk factors on cognitive decline or dementia. We quantified loss of information using the R-squared of linear regression models with pooled variables as a function of their original variable(s). In case the R-squared was below 0.8, we additionally explored the potential impact of loss of information for future analyses. We did this second step by comparing whether the Beta coefficient of the predictor differed more than 10% when adding original or recoded variables as a confounder in a linear regression model. In a simulation we randomly sampled numbers, recoded those < = 1000 to 0 and those >1000 to 1 and varied the range of the continuous variable, the ratio of recoded zeroes to recoded ones, or both, and again extracted the R-squared from linear models to quantify information loss.ResultsThe R-squared was below 0.8 for 8 out of 91 recoded variables. In 4 cases this had a substantial impact on the regression models, particularly when a continuous variable was recoded into a discrete variable. Our simulation showed that the least information is lost when the ratio of recoded zeroes to ones is 1:1.ConclusionsLarge, pooled datasets provide great opportunities, justifying the efforts for data harmonization. Still, caution is warranted when using recoded variables which variance is explained limitedly by their original variables as this may jeopardize the validity of study results. ]]> <![CDATA[A descriptive cross sectional study comparing barriers and determinants of physical activity of Sri Lankan middle aged and older adults]]> https://www.researchpad.co/article/elastic_article_7830 Benefits of physical activities are numerous. Barriers for physical exercise may differ among middle aged and older adults. Therefore, identifying and comparing the barriers for participating in regular physical exercises among middle aged and older adults will be useful in designing age specific physical exercise programmes.MethodsThis descriptive cross sectional study was carried out among 206 Sri Lankan adults in the age range of 40–84 years in the Colombo North region of Sri Lanka using culturally validated questionnaires to determine and compare the barriers and factors associated with regular physical activity participation. Majority were males (56%) and 54% were < 60 years. People in the age range of 40–59 years were considered as middle age and ≥ 60 years as older adults. Bivariate analysis and multivariate analysis was carried out to determine the significant factors that are associated with regular physical activity participation.ResultsLack of free time (52%), feeling too lazy (26%) and bad weather (29%) were the main barriers for the participants. In < 60 years, high level of income (p = 0.008) and in ≥ 60 years, being a male (p = 0.016), having a high level of education (P = 0.002) and a high BMI (p = 0.002) had a significant negative association with the level of physical activities.ConclusionsContrary to findings from surveys in several developed countries, this study showed that having a high level of education and being a male were strongly related with lack of physical activity participation. ]]> <![CDATA[Image-quality metric system for color filter array evaluation]]> https://www.researchpad.co/article/elastic_article_7704 A modern color filter array (CFA) output is rendered into the final output image using a demosaicing algorithm. During this process, the rendered image is affected by optical and carrier cross talk of the CFA pattern and demosaicing algorithm. Although many CFA patterns have been proposed thus far, an image-quality (IQ) evaluation system capable of comprehensively evaluating the IQ of each CFA pattern has yet to be developed, although IQ evaluation items using local characteristics or specific domain have been created. Hence, we present an IQ metric system to evaluate the IQ performance of CFA patterns. The proposed CFA evaluation system includes proposed metrics such as the moiré robustness using the experimentally determined moiré starting point (MSP) and achromatic reproduction (AR) error, as well as existing metrics such as color accuracy using CIELAB, a color reproduction error using spatial CIELAB, structural information using the structure similarity, the image contrast based on MTF50, structural and color distortion using the mean deviation similarity index (MDSI), and perceptual similarity using Haar wavelet-based perceptual similarity index (HaarPSI). Through our experiment, we confirmed that the proposed CFA evaluation system can assess the IQ for an existing CFA. Moreover, the proposed system can be used to design or evaluate new CFAs by automatically checking the individual performance for the metrics used.

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<![CDATA[Multipurpose chemical liquid sensing applications by microwave approach]]> https://www.researchpad.co/article/elastic_article_7700 In this work, a novel sensor based on printed circuit board (PCB) microstrip rectangular patch antenna is proposed to detect different ratios of ethanol alcohol in wines and isopropyl alcohol in disinfectants. The proposed sensor was designed by finite integration technique (FIT) based high-frequency electromagnetic solver (CST) and was fabricated by Proto Mat E33 machine. To implement the numerical investigations, dielectric properties of the samples were first measured by a dielectric probe kit then uploaded into the simulation program. Results showed a linear shifting in the resonant frequency of the sensor when the dielectric constant of the samples were changed due to different concentrations of ethanol alcohol and isopropyl alcohol. A good agreement was observed between the calculated and measured results, emphasizing the usability of dielectric behavior as an input sensing agent. It was concluded that the proposed sensor is viable for multipurpose chemical sensing applications.

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