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Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always has been a research subject in recent years. Being simple and cost-effective, electrocardiogram-based commercial products can be found in the market that provides cardiac diagnostic information for assessment, including heart rate measurement and atrial fibrillation identification. Based on a data-driven and self-adaptive approach, this study aims to estimate heart rate and respiratory rate simultaneously from one lead electrocardiogram signal. In contrast to ensemble empirical mode decomposition with principle component analysis, performed in the time domain, our method uses spectral data fusion, together with intrinsic mode functions using ensemble empirical mode decomposition obtains a more accurate heart rate and respiratory rate. Equipped with a rule-based selection of defined frequency levels for respiratory rate (RR) estimation, the proposed method obtains (0.92, 1.32) beat per minute for the heart rate and (2.20, 2.92) breath per minute for the respiratory rate as their mean absolute error and root mean square error, respectively outperforming other existing methods.

A new fluorescent arrayed biosensor has been developed to discriminate species and concentrations of target proteins by using plural different phospholipid liposome species encapsulating fluorescent molecules, utilizing differences in permeation of the fluorescent molecules through the membrane to modulate liposome-target protein interactions. This approach proposes a basically new label-free fluorescent sensor, compared with the common technique of developed fluorescent array sensors with labeling. We have confirmed a high output intensity of fluorescence emission related to characteristics of the fluorescent molecules dependent on their concentrations when they leak from inside the liposomes through the perturbed lipid membrane. After taking an array image of the fluorescence emission from the sensor using a CMOS imager, the output intensities of the fluorescence were analyzed by a principal component analysis (PCA) statistical method. It is found from PCA plots that different protein species with several concentrations were successfully discriminated by using the different lipid membranes with high cumulative contribution ratio. We also confirmed that the accuracy of the discrimination by the array sensor with a single shot is higher than that of a single sensor with multiple shots.

Our research aim was to apply UV/Vis spectrophotometric techniques for the rapid monitoring of the quality of water sourced from on-farm root vegetable washing processes. To achieve this goal, the quality assessment of the washing water and wastewater at different stages of the technological processes was performed using physicochemical, biological, and UV/Vis absorbance measurements as well as statistical methods, such as principal component analysis (PCA) and partial least squares (PLS) regression. Limit values of UV/Vis absorbance at specific wavelengths were predicted in order to adapt them for routine testing and water quality monitoring at the farm packhouses. Results of the lab analyses showed, that the main problems of the water quality were caused by suspended solids (470–3400 mg L−1), organic substances (BOD5 215–2718 mg L−1; COD 540–3229 mg L−1), nitrogen (3–52 mg L−1), phosphorus (1–6 mg L−1), and pathogenic microorganisms (TVC > 300 cfu mL−1, E. coli 5.5 × 103–1.0 × 104 cfu mL−1, intestinal enterococci 2.8 × 102–1.5 × 104 cfu mL−1, coliform bacteria 1.6 × 103–2.0 × 104 cfu mL−1). Suspended solids exceeded the limit values by 10–50 times, organic matter by 10–25 times, dissolved organic carbon by 3–5 times, nitrogen by 3–7 times, total phosphorus by 3–12 times, and microorganisms by 3–10 times. UV/Vis limit values calculated were as follows: A210 nm—3.997–4.009 cm−1, A 240 nm—5.193–5.235 cm−1, A254 nm—4.042–4.047 cm−1, A320 nm—7.387–7.406 cm−1, and A 660 nm—3.937–3.946 cm−1. UV/Vis measurements at A320 nm are proposed for the routine water quality monitoring.

Current environmental and health concerns encourage a shift towards more sustainable diets. A variety of options are currently being investigated to achieve the food security of alternative-to-meat dietary proteins. The food security of alternative to meat proteins will require attention to the availability, the access, the supply stability and the food safety and quality. The aim of this research is to get insight on consumers’ food attitudes in order to achieve food security of four alternatives to meat proteins, namely, plant-based proteins, mycoproteins, cultured meat proteins and insect proteins in different development contexts in Spain and the Dominican Republic. In doing so, the research analyses meat consumption, reduces consumers’ attitudes using a principal component analysis, predicts first adopters of alternative dietary proteins using a Chi-square test and ranks preferred alternative dietary proteins using a multicriteria decision-making method. The results show that plant-based proteins are the best positioned alternative, while insects are the worst positioned in the Dominican Republic. Gender and education in the Dominican Republic and gender, education and age in Spain are significant factors for the adoption of alternative to meat proteins. Health and convenience attitudes may determine the adoption of alternative dietary proteins in Spain and the Dominican Republic. This research contributes to identifying the consumers’ attitudes to encourage the dietary shift to alternative to meat proteins. It can help industry to market alternative-to-meat proteins in different development contexts to achieve food security.

Vertical deformations of the Earth’s surface result from a host of geophysical and geological processes. Identification and assessment of the induced signals is key to addressing outstanding scientific questions, such as those related to the role played by the changing climate on height variations. This study, focused on the European and Mediterranean area, analyzed the GPS height time series of 114 well-distributed stations with the aim of identifying spatially coherent signals likely related to variations of environmental parameters, such as atmospheric surface pressure (SP) and terrestrial water storage (TWS). Linear trends and seasonality were removed from all the time series before applying the principal component analysis (PCA) to identify the main patterns of the space/time interannual variability. Coherent height variations on timescales of about 5 and 10 years were identified by the first and second mode, respectively. They were explained by invoking loading of the crust. Single-value decomposition (SVD) was used to study the coupled interannual space/time variability between the variable pairs GPS height–SP and GPS height–TWS. A decadal timescale was identified that related height and TWS variations. Features common to the height series and to those of a few climate indices—namely, the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI)—were also investigated. We found significant correlations only with the MEI. The first height PCA mode of variability, showing a nearly 5-year fluctuation, was anticorrelated (−0.23) with MEI. The second mode, characterized by a decadal fluctuation, was well correlated (+0.58) with MEI; the spatial distribution of the correlation revealed, for Europe and the Mediterranean area, height decrease till 2015, followed by increase, while Scandinavian and Baltic countries showed the opposite behavior.

Martins, K.A.; Pereira, P.S.; Esteves, L.S., and Williams, J., 2019. The role of coral reefs in coastal protection: Analysis of beach morphology. In: Silva, R.; Martínez, M.L.; Chávez, V., and Lithgow, D. (eds.), Integrating Biophysical Components in Coastal Engineering Practices. Journal of Coastal Research, Special Issue No. 92, pp. 157–164. Coconut Creek (Florida), ISSN 0749-0208. This paper evaluates the effect of a fringing reef on the morphodynamic behaviour of adjacent beaches in terms of profile stability and cross-shore sediment exchange. Variations in subaerial beach morphology along 39 cross-shore profiles at Pontal do Cupe beach (Northeastern Brazil) were analysed, using modelled wave data and monthly beach topography acquired from November 2014 to September 2016. Pontal do Cupe has a reef to the south but is exposed to waves in the north, making this an ideal location to assess the sheltering effect of the reef. Beach volume and beach width data were used to compare the reef-fronted profiles with those of the exposed adjacent beach. Seven groups of profiles were identified by applying Principal Component Analys is to the topography dataset. A simple numerical model was used to quantify the role of the reef in dissipating wave energy, showing a reduction of approximately 50% in incoming wave energy to the shore. The reef-fronted beach is significantly more stable than the exposed beach. Total beach volume is similar for both the exposed and the reef-fronted beach. The results of this survey can be used as a proxy for the ecosystem service of coastal protection provided by reefs.

BACKGROUND AND OBJECTIVES: Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, future PM10 concentration forecasting is important for early prevention and in urban development planning, which is crucial for developing cities. This paper presents the development of PM10 forecasting model using nonlinear autoregressive with exogenous input model.METHODS: To improve performance of nonlinear autoregressive with exogenous input model, principal component analysis is used prior to the model for variable selection. The first stage of principal component analysis involves Scree plot, which determines the number of principal components based on explained variance. This is then followed by selecting variables using a rotated component matrix, based on their strength of contribution towards variation of PM10 concentration. To test the model, PM10 data in Kota Kinabalu from 2003 – 2010 was used. Neural network models are developed using this data by varying number of input variables with the inclusion of temporal variables. The developed forecasting models are evaluated using data PM10 in the city from 2011 to 2012. Four performance indicators, namely root mean square error, mean absolute error, index of agreement and fractional bias are reported.FINDINGS: Results from principal component analysis show that five variables including wind direction index, relative humidity, ambient temperature, concentration of nitrogen dioxide and concentration of ozone strongly contribute to the variation of PM10 concentration.  By using these variables together with temporal variables as input in the nonlinear autoregressive with exogenous input models, the resultant model shows good forecasting performance, with root mean square error of 7.086±0.873 µg/m3. The selection of significant variables helps in reducing input variables inside the forecast model without degrading its forecast performance.CONCLUSION: This model shows very promising performance in forecasting PM10 concentration in Kota Kinabalu as it requires fewer input variables and does not require variable transformation.

The objectives of this study were to estimate the performance of the common bean (Phaseolus vulgaris L.)  genotypes under water-stress conditions, and their genetic diversity. White bean surpassed the others for relative water content, root/shoot ratio and leaf area under water-stress condition. Scatter plot indicates a strong association of yield with pod numbers plant-1, branch number and harvest index. A total of 69 polymorphic were obtained, applying 26 SSR primers on 14 genotypes. Major allele frequency was 0.601, and the average value of PIC was 0.407. The highest value of gene diversity (0.745) and PIC (0.704) were recorded for BMd-23 marker. Molecular variance among population indicated 25%, while 47% was realized within populations. Structure analysis divided the common bean genotypes into three groups (DeltaK value =3). Chity and Boschbohnen were identified to have a mixed ancestor while all the others were pure at their populations. A dendrogram and PCoA analyses are accordingly indicated three groups of the genotypes based on SSR marker data. STRUCTURE, UPGMA and PCoA analysis revealed the presence of two separated gene pools of Andean and Mesoamerican common beans, with a high level of genetic differentiation (FST value=0.250). Both phenotypic and molecular genetic outcomes here would accelerate future improvement programs.

Multidimensional exploratory techniques, such as the Principal Component Analysis (PCA), have been used to analyze long-term changes in the flow regime and quality of water of the lowland dam reservoir Turawa (south-west Poland) in the catchment of the Mała Panew river (a tributary of the Odra). The paper proves that during the period of 1998–2016 the Turawa reservoir was equalizing the river’s water flow. Moreover, various physicochemical water quality indicators were analyzed at three measurement points (at the tributary’s mouth into the reservoir, in the reservoir itself and at the outflow from the reservoir). The water quality assessment was performed by analyzing physicochemical indicators such as water temperature, TSS, pH, dissolved oxygen, BOD5, NH4+, NO3-, NO2-, N, PO43-, P, electrolytic conductivity, DS, SO42- and Cl- . Furthermore, the correlations between all these water quality indicators were analyzed statistically at each measurement point, at the statistical signifi cance level of p ≤ 0.05. PCA was used to determine the structures between these water quality variables at each measurement point. As a result, a theoretical model was obtained that describes the regularities in the relationships between the indicators. PCA has shown that biogenic indicators have the strongest influence on the water quality in the Mała Panew. Lastly, the differences between the averages of the water quality indicators of the inflowing and of the outflowing water were considered and their significance was analyzed. PCA unveiled structure and complexity of interconnections between river flow and water quality. The paper shows that such statistical methods can be valuable tools for developing suitable water management strategies for the catchment and the reservoir itself.

Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details. Therefore, in this paper, the basics of ICA are provided to show how it works to serve as a comprehensive source for researchers who are interested in this field. This paper starts by introducing the definition and underlying principles of ICA. Additionally, different numerical examples in a step-by-step approach are demonstrated to explain the preprocessing steps of ICA and the mixing and unmixing processes in ICA. Moreover, different ICA algorithms, challenges, and applications are presented.