Approbation of the texture analysis imaging technique in the wastewater treatment plant

publishedVersio

Comparison of anxiety symptoms in spouses of persons suffering from dementia, geriatric in-patients and healthy older persons

Fagfellevurdert, vitenskapelig tidsskriftartikkelObjective:To describe and compare anxiety symptoms in spouses of persons suffering from dementia, geriatric in-patients and healthy controls, and to study possible risk factors associated with anxiety in these groups of older people. Method: The participants were 70 years and above: 1) 76 spouses of persons with dementia recruited from a memory clinic, 2) 98 in-patients without dementia but suffering from one or more chronic diseases, who were admitted to a geriatric department of an acute hospital, and 3) 68 healthy elderly people recruited from day-centres. The State-Trait Anxiety Inventory (STAI-X-1, 12-item) was used to tap anxiety symptoms. Results: Spouses of persons suffering from dementia expressed the same degree of anxiety symptoms as geriatric patients, and anxiety in these two groups differed significantly from the healthy elderly persons. In an adjusted linear regression analysis, anxiety, expressed as a high score on STAI-X-1, was associated with female gender (ß 0.16, p=0.01); being a spousal carer (ß 0.49, p <0.001) and being a geriatric patient (ß 0.57, p<0.001). Conclusion: Spouses of persons suffering from dementia reported as much anxiety symptoms as geriatric in-patients and both groups reported significantly more symptoms of anxiety than healthy older persons without caring obligations. The mental health nurses should include assessment of carers’ anxiety as routine

Daylength influences the response of three clover species (Trifolium spp.) to short-term ozone stress

-Long photoperiods characteristic of summers at high latitudes can increase ozone-induced foliar injury in subterranean clover (Trifolium subterraneum) This study compared the effects of long photoperiods on ozone injury in red and white clover cultivars adapted to shorter or longer daylengths of southern or northern Fennoscandia. Plants were exposed to 70 ppb ozone for six hours during the daytime for three consecutive days. Simultaneously, the daylength in the growth rooms was altered to long-day (10 h light; 14 h dim light) and short-day (10 h light; 14 h darkness) conditions. Thermal imaging showed that ozone disrupted leaf temperature and stomatal function, particularly in sensitive species, in which leaf temperature deviations persisted for several days after ozone exposure. Longday conditions increased visible foliar injury (30%–70%), characterized by chlorotic and necrotic areas, relative to short day conditions in all species and cultivars independently of the photoperiod in the region they were adapted to

Evaluating Flood Exposure for Properties in Urban Areas Using a Multivariate Modelling Technique

Urban flooding caused by heavy rainfall is expected to increase in the future. The main purpose of this study was to investigate the variables characterizing the placement of a house, which seem to have an impact when it comes to the exposure to floods. From the same region in Norway, data from 347 addresses were derived. All addresses were either associated with insurance claims caused by flooding or were randomly selected. A multivariate statistical model, Partial Least Square Regression (PLS), was used. Among others, the analysis has shown that the upstream, sealed area is the most significant variable for characterizing properties’ exposure to urban flooding. The model confirms that flooding tends to occur near old combined sewer mains and in concave curvature, and houses located in steep slopes seem to be less exposed. Using this method, it is possible to rank and quantify significant exposure variables contributing to urban floods within a region. Results from the PLS-analysis might provide important input to professionals, when planning and prioritizing measures. It can also predict flood-prone areas and make residents aware of the risks, which may induce them to implement preventive measures.publishedVersio

Pixel classification methods for identifying and quantifying leaf surface injury from digital images

Plants exposed to stress due to pollution, disease or nutrient deficiency often develop visible symptoms on leaves such as spots, colour changes and necrotic regions. Early symptom detection is important for precision agriculture, environmental monitoring using bio-indicators and quality assessment of leafy vegetables. Leaf injury is usually assessed by visual inspection, which is labour-intensive and to a consid- erable extent subjective. In this study, methods for classifying individual pixels as healthy or injured from images of clover leaves exposed to the air pollutant ozone were tested and compared. RGB images of the leaves were acquired under controlled conditions in a laboratory using a standard digital SLR camera. Different feature vectors were extracted from the images by including different colour and texture (spa- tial) information. Four approaches to classification were evaluated: (1) Fit to a Pattern Multivariate Image Analysis (FPM) combined with T2 statistics (FPM-T2) or (2) Residual Sum of Squares statistics (FPM-RSS), (3) linear discriminant analysis (LDA) and (4) K-means clustering. The predicted leaf pixel classifications were trained from and compared to manually segmented images to evaluate classification performance. The LDA classifier outperformed the three other approaches in pixel identification with significantly higher accuracy, precision, true positive rate and F-score and significantly lower false positive rate and computation time. A feature vector of single pixel colour channel intensities was sufficient for capturing the information relevant for pixel identification. Including neighbourhood pixel information in the feature vector did not improve performance, but significantly increased the computation time. The LDA classifier was robust with 95% mean accuracy, 83% mean true positive rate and 2% mean false positive rate, indicating that it has potential for real-time applications.Opstad Kruse, OM.; Prats Montalbán, JM.; Indahl, UG.; Kvaal, K.; Ferrer Riquelme, AJ.; Futsaether, CM. (2014). Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Computers and Electronics in Agriculture. 108:155-165. doi:10.1016/j.compag.2014.07.010S15516510

Explicit and interpretable nonlinear soft sensor models for influent surveillance at a full-scale wastewater treatment plant

In wastewater treatment plants, the most adopted sensors are those with the properties of low cost and fast response. Soft sensors are alternative solutions to the hardware sensor for online monitoring of hard-tomeasure variables, such as chemical oxygen demand (COD) and total phosphorus (TP). The purpose of this study is to obtain a modelling approach which is able to identify the nonlinearity of influent and explain the correlation of inputs-outputs. Thus, the variation of influent characteristics was investigated at the first stage, which provided the basis to build global and local multiple linear regression models. Secondly, a nonlinear modelling tool multivariate adaptive regression splines (MARS) was applied for influent COD and TP prediction. Satisfactory prediction accuracy was obtained in terms of root mean square error (RMSE) and R2. Unlike other machine learning techniques which are “black box” models, MARS provided interpretable models which explained the nonlinearity and correlation of inputs-outputs. The MARS models can be used not only for prediction, but also to provide insight of influent variation.acceptedVersio

Detection and Quantification of Tire Particles in Sediments Using a Combination of Simultaneous Thermal Analysis, Fourier Transform Infra-Red, and Parallel Factor Analysis

publishedVersio

Comparison of anxiety symptoms in spouses of persons suffering from dementia, geriatric in-patients and healthy older persons

Objective:To describe and compare anxiety symptoms in spouses of persons suffering from dementia, geriatric in-patients and healthy controls, and to study possible risk factors associated with anxiety in these groups of older people. Method: The participants were 70 years and above: 1) 76 spouses of persons with dementia recruited from a memory clinic, 2) 98 in-patients without dementia but suffering from one or more chronic diseases, who were admitted to a geriatric department of an acute hospital, and 3) 68 healthy elderly people recruited from day-centres. The State-Trait Anxiety Inventory (STAI-X-1, 12-item) was used to tap anxiety symptoms. Results: Spouses of persons suffering from dementia expressed the same degree of anxiety symptoms as geriatric patients, and anxiety in these two groups differed significantly from the healthy elderly persons. In an adjusted linear regression analysis, anxiety, expressed as a high score on STAI-X-1, was associated with female gender (ß 0.16, p=0.01); being a spousal carer (ß 0.49, p <0.001) and being a geriatric patient (ß 0.57, p<0.001). Conclusion: Spouses of persons suffering from dementia reported as much anxiety symptoms as geriatric in-patients and both groups reported significantly more symptoms of anxiety than healthy older persons without caring obligations. The mental health nurses should include assessment of carers’ anxiety as routine

Evaluation of image texture recognition techniques in application to wastewater coagulation

Flocs formation and growth are important characteristics in wastewater coagulation process. The shape and size of flocs highly affect further separation processes, therefore resulting treatment efficiency of wastewater after coagulation. Observed images of flocs tend to show strong relations to coagulation parameters: dose and coagulation time. In this article, three texture recognition techniques were evaluated for the ability to mathematically describe the relationship between the images of flocs and coagulant dosages. The easily computable texture analysis methods were found to be potential techniques for the characterization of the particles images. Ten out of eleven co-occurrence matrix-based grey level co-occurrence matrix (GLCM) texture features were found to be significant for the dosage prediction by a principal component regression model with only one principal component. Two features (Inverse difference moment and Variance) were selected for the multiple linear regression model. Test set prediction accuracy varied from 83 to 96% depending on texture analysis method and multivariate model. Best dosage prediction and image classification results were achieved by GLCM and angle measure technique. The results of image texture analysis coupled with multivariate modelling techniques indicate that it is possible to characterize and relate flocs images, captured during coagulation, with different coagulant dosages, as well as predict those dosages