A Bio-Wicking System to Mitigate Capillary Water in Base Course

Water within pavement layers is the major cause of pavement deteriorations. High water content results in significant reduction in soil’s resilient behavior and increase in permanent deformation. Conventional drainage systems can only drain gravity water but not capillary water. Both preliminary lab and field tests have proven the drainage efficiency of a newly developed H2Ri geotextile with wicking fabrics. This bio-wicking system aims at resolving the potential issues that the original design may encounter: (1) H2Ri ultraviolet degradation, (2) H2Ri mechanical failure, (3) loss of drainage function under high suction, and (4) clogging and salt concentration. Both elemental level and full-scale test results indicated that the bio-wicking system is more effective in draining capillary water within the base courses compared with original design, in which the geotextile is directly exposed to the open air. However, a good drainage condition is required for the bio-wicking system to maintain its drainage efficiency. Accumulation of excess water will result in water re-entering the road embankment. Moreover, grass root and geotextile share the same working mechanism in transporting water. In the proposed bio-wicking system, the relatively smaller channels in the grass roots further ensures water moving from H2Ri geotextile, transporting through the stems of grass, and eventually evapo-transpiring into the air at the leaf-air interfaces. In sum, the bio-wicking system seemed to successfully address the concerns in the preliminary design and is a more efficient system to dehydrate the road embankment under unsaturated conditions.TenCate Geosynthetic

Instrumentation for Biological Research, Volume I, Sections 1 to 3 Final Report, Nov. 9, 1964 - Mar. 31, 1966

Bioinstrumentation for controlling and measuring parameters interacting with biological syste

Space Station Centrifuge: A Requirement for Life Science Research

A centrifuge with the largest diameter that can be accommodated on Space Station Freedom is required to conduct life science research in the microgravity environment of space. (This was one of the findings of a group of life scientists convened at the University of California, Davis, by Ames Research Center.) The centrifuge will be used as a research tool to understand how gravity affects biological processes; to provide an on-orbit one-g control; and to assess the efficacy of using artificial gravity to counteract the deleterious biological effect of space flight. The rationale for the recommendation and examples of using ground-based centrifugation for animal and plant acceleration studies are presented. Included are four appendixes and an extensive bibliography of hypergravity studies

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop

Can we detect harmony in artistic compositions? A machine learning approach

Harmony in visual compositions is a concept that cannot be defined or easily expressed mathematically, even by humans. The goal of the research described in this paper was to find a numerical representation of artistic compositions with different levels of harmony. We ask humans to rate a collection of grayscale images based on the harmony they convey. To represent the images, a set of special features were designed and extracted. By doing so, it became possible to assign objective measures to subjectively judged compositions. Given the ratings and the extracted features, we utilized machine learning algorithms to evaluate the efficiency of such representations in a harmony classification problem. The best performing model (SVM) achieved 80% accuracy in distinguishing between harmonic and disharmonic images, which reinforces the assumption that concept of harmony can be expressed in a mathematical way that can be assessed by humans.Comment: 9 pages, ICAART 202

Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant

The Victoria Amazonica plant, often known as the Giant Water Lily, has the largest floating spherical leaf in the world, with a maximum leaf diameter of 3 meters. It spreads its leaves by the force of its spines and creates a large shadow underneath, killing any plants that require sunlight. These water tyrants use their formidable spines to compel each other to the surface and increase their strength to grab more space from the surface. As they spread throughout the pond or basin, with the earliest-growing leaves having more room to grow, each leaf gains a unique size. Its flowers are transsexual and when they bloom, Cyclocephala beetles are responsible for the pollination process, being attracted to the scent of the female flower. After entering the flower, the beetle becomes covered with pollen and transfers it to another flower for fertilization. After the beetle leaves, the flower turns into a male and changes color from white to pink. The male flower dies and sinks into the water, releasing its seed to help create a new generation. In this paper, the mathematical life cycle of this magnificent plant is introduced, and each leaf and blossom are treated as a single entity. The proposed bio-inspired algorithm is tested with 24 benchmark optimization test functions, such as Ackley, and compared to ten other famous algorithms, including the Genetic Algorithm. The proposed algorithm is tested on 10 optimization problems: Minimum Spanning Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color Quantization, and Image Segmentation and compared to traditional and bio-inspired algorithms. Overall, the performance of the algorithm in all tasks is satisfactory.Comment: 45 page

Parameter sensitivity analysis for different complexity land surface models using multicriteria methods

A multicriteria algorithm, the MultiObjective Generalized Sensitivity Analysis (MOGSA), was used to investigate the parameter sensitivity of five different land surface models with increasing levels of complexity in the physical representation of the vegetation (BUCKET, CHASM, BATS 1, Noah, and BATS 2) at five different sites representing crop land/ pasture, grassland, rain forest, cropland, and semidesert areas. The methodology allows for the inclusion of parameter interaction and does not require assumptions of independence between parameters, while at the same time allowing for the ranking of several single-criterion and a global multicriteria sensitivity indices. The analysis required on the order of 50 thousand model runs. The results confirm that parameters with similar "physical meaning" across different model structures behave in different ways depending on the model and the locations. It is also shown that after a certain level an increase in model structure complexity does not necessarily lead to better parameter identifiability, i.e., higher sensitivity, and that a certain level of overparameterization is observed. For the case of the BATS 1 and BATS 2 models, with essentially the same model structure but a more sophisticated vegetation model, paradoxically, the effect on parameter sensitivity is mainly reflected in the sensitivity of the soil-related parameter. Copyright 2006 by the American Geophysical Union