Semi-closed greenhouses have been developed in which window ventilation is minimized due to active cooling, enabling enhanced CO2 concentrations at high irradiance. Cooled and dehumidified air is blown into the greenhouse from below or above the canopy. Cooling below the canopy may induce vertical temperature gradients along the length of the plants. Our first aim was to analyze the effect of the positioning of the inlet of cooled and dehumidified air on the magnitudes of vertical temperature and VPD gradients in the semi-closed greenhouses. The second aim was to investigate the effects of vertical temperature gradients on assimilate production, partitioning, and fruit growth. Tomato crops were grown year-round in four semiclosed greenhouses with cooled and dehumidified air blown into the greenhouses from below or above the crop. Cooling below the canopy induced vertical temperature and VPD gradients. The temperature at the top of the canopy was over 5°C higher than at the bottom, when outside solar radiation was high (solar radiation >250 J cm-2 h-1). Total dry matter production was not affected by the location of the cooling (4.64 and 4.80 kg m-2 with cooling from above and from below, respectively). Percentage dry matter partitioning to the fruits was 74% in both treatments. Average over the whole growing season the fresh fruit weight of the harvested fruits was not affected by the location of cooling (118 vs 112 g fruit-1). However, during summer period the average fresh fruit weight of the harvested fruits in the greenhouse with cooling from below was higher than in the greenhouse with cooling from above (124 vs 115 g fruit-1).
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Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
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Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
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On the basis of a model for energy budgets, which includes the dynamics of stored energy, a model has been proposed for the kinetics of non-metabolized xenobiotic compounds, which may be lipophilic. The surface area coupled uptake is via food and/or water through the aqueous fraction of the animal. The partitioning to non-aqueous structural body mass and to stored materials (i.e. lipids, carbohydrates and proteins) is taken instantaneously. The result is a simple first order kinetics with variable coefficients. The bioconcentration factor has been evaluated. Model predictions have been tested against data from the litera
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Plant photosynthesis and biomass production are associated with the amount of intercepted light, especially the light distribution inside the canopy. Three virtual canopies (n = 80, 3.25 plants/m2) were constructed based on average leaf size of the digitized plant structures: ‘small leaf’ (98.1 cm2), ‘medium leaf’ (163.0 cm2) and ‘big leaf’ (241.6 cm2). The ratios of diffuse light were set in three gradients (27.8%, 48.7%, 89.6%). The simulations of light interception were conducted under different ratios of diffuse light, before and after the normalization of incident radiation. With 226.1% more diffuse light, the result of light interception could increase by 34.4%. However, the 56.8% of reduced radiation caused by the increased proportion of diffuse light inhibited the advantage of diffuse light in terms of a 26.8% reduction in light interception. The big-leaf canopy had more mutual shading effects, but its larger leaf area intercepted 56.2% more light than the small-leaf canopy under the same light conditions. The small-leaf canopy showed higher efficiency in light penetration and higher light interception per unit of leaf area. The study implied the 3D structural model, an effective tool for quantitative analysis of the interaction between light and plant canopy structure.
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Het doel van dit onderzoek is te onderzoeken onder welke omstandigheden en onder welke condities relatief moderne modelleringstechnieken zoals support vector machines, neural networks en random forests voordelen zouden kunnen hebben in medisch-wetenschappelijk onderzoek en in de medische praktijk in vergelijking met meer traditionele modelleringstechnieken, zoals lineaire regressie, logistische regressie en Cox regressie.
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Individuals with autism increasingly enroll in universities, but little is known about predictors for their success. This study developed predictive models for the academic success of autistic bachelor students (N=101) in comparison to students with other health conditions (N=2465) and students with no health conditions (N=25,077). We applied propensity score weighting to balance outcomes. The research showed that autistic students’ academic success was predictable, and these predictions were more accurate than predictions of their peers’ success. For first-year success, study choice issues were the most important predictors (parallel program and application timing). Issues with participation in pre-education (missingness of grades in pre-educational records) and delays at the beginning of autistic students’ studies (reflected in age) were the most influential predictors for the second-year success and delays in the second and final year of their bachelor’s program. In addition, academic performance (average grades) was the strongest predictor for degree completion in 3 years. These insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students.
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Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
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Field experiments were carried out in 1991 and 1992 on sandy soil highly infested with the potato cyst nematode Globodera pallida. Half the trial area was fumigated with nematicide to establish two levels of nematode density. Three levels of soil compaction were made by different combinations of artificial compaction and rotary cultivation. Two potato cultivars were used in 1991 and four in 1992. Both high nematode density and soil compaction caused severe yield losses, of all cultivars except cv. Elles which was tolerant of nematode attack. The effects of the two stress factors were generally additive. Analysis of the yield loss showed that nematodes mainly reduced cumulative interception of light while compaction mainly reduced the efficiency with which intercepted light was used to produce biomass. This indicates that nematodes and compaction affect growth via different damage mechanisms. Nematodes reduced light interception by accelerating leaf senescence, by decreasing the specific leaf area and indirectly by reducing overall crop growth rate. Partitioning of biomass between leaves, stems and tubers was not affected by nematode infestation but compaction decreased partitioning to leaves early in the growing season while increasing it during later growth stages. The effects of nematodes and compaction on root length dynamics and nutrient uptake were also additive. This suggests that the commonly observed variation in yield loss caused by nematodes on different soil types is not related to differences in root system expansion between soils of various strength. Cv. Elles, which showed tolerance of nematodes by relatively low yield losses in both experiments, was characterised by high root length density and thick roots. These characteristics did not confer tolerance of soil compaction, since compaction affected root lengths and tuber yields equally in all cultivars. In the first experiment only, high nematode density led to decreased root lengths and lower plant nutrient concentrations. The yield loss which occurred in the second experiment was attributed to the effects of nematodes on other aspects of plant physiology. Copyright © 1995, Wiley Blackwell. All rights reserved
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This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers.
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