Soil and water bioengineering: practice and research needs for reconciling natural hazard control and ecological restoration

Soil and water bioengineering is a technology that encourages scientists and practitioners to combine their knowledge and skills in the management of ecosystems with a common goal to maximize benefits to both man and the natural environment. It involves techniques that use plants as living building materials, for: (i) natural hazard control (e.g., soil erosion, torrential floods and landslides) and (ii) ecological restoration or nature-based re-introduction of species on degraded lands, river embankments, and disturbed environments. For a bioengineering project to be successful, engineers are required to highlight all the potential benefits and ecosystem services by documenting the technical, ecological, economic and social values. The novel approaches used by bioengineers raise questions for researchers and necessitate innovation from practitioners to design bioengineering concepts and techniques. Our objective in this paper, therefore, is to highlight the practice and research needs in soil and water bioengineering for reconciling natural hazard control and ecological restoration. Firstly, we review the definition and development of bioengineering technology, while stressing issues concerning the design, implementation, and monitoring of bioengineering actions. Secondly, we highlight the need to reconcile natural hazard control and ecological restoration by posing novel practice and research questions

An Evolutionary Neural Network Approach for Slopes Stability Assessment

A current big challenge for developed or developing countries is how to keep large-scale transportation infrastructure networks operational under all conditions. Network extensions and budgetary constraints for maintenance purposes are among the main factors that make transportation network management a non-trivial task. On the other hand, the high number of parameters affecting the stability condition of engineered slopes makes their assessment even more complex and difficult to accomplish. Aiming to help achieve the more efficient management of such an important element of modern society, a first attempt at the development of a classification system for rock and soil cuttings, as well as embankments based on visual features, was made in this paper using soft computing algorithms. The achieved results, although interesting, nevertheless have some important limitations to their successful use as auxiliary tools for transportation network management tasks. Accordingly, we carried out new experiments through the combination of modern optimization and soft computing algorithms. Thus, one of the main challenges to overcome is related to the selection of the best set of input features for a feedforward neural network for earthwork hazard category (EHC) identification. We applied a genetic algorithm (GA) for this purpose. Another challenging task is related to the asymmetric distribution of the data (since typically good conditions are much more common than bad ones). To address this question, three training sampling approaches were explored: no resampling, the synthetic minority oversampling technique (SMOTE), and oversampling. Some relevant observations were taken from the optimization process, namely, the identification of which variables are more frequently selected for EHC identification. After finding the most efficient models, a detailed sensitivity analysis was applied over the selected models, allowing us to measure the relative importance of each attribute in EHC identification

Selecting and Propagating Clones of Bigtooth Maple (\u3ci\u3eAcer grandidentatum\u3c/i\u3e Nutt.)

Numerous wild bigtooth maple (Acer grandidentatum Nutt.) specimens in northern Utah have potential for use in landscapes, but improvements in selection and propagation need to be developed before these specimens can be introduced to the green industry. Criteria-based evaluations centered on aesthetics, function, and fall color were performed to objectively select superior bigtooth maple specimens. Out of 56 trees initially selected for red fall color, six were selected for propagation based on all three criteria. Five of the six selected trees yielded viable bud take via chip budding. Optimum time for chip budding propagation was determined by four experiments. Coppiced seedling rootstocks were used with the return budding of excised buds as scions to parent stock (2006) and grafting buds from wild trees as scions (2007 and 2009). A fourth experiment examined chip budding of wild scions on 2-year-old, containerized, seedling rootstocks. The general time period identified as the optimum time for budding bigtooth maple was July through mid-August. Propagation by cuttings was also explored as an alternative production method among bigtooth maple selections. Softwood cuttings were taken from six selections of wild bigtooth maples grafted on seedling rootstocks growing in a coppiced stool bed environment. Open-ended, black, velour, drawstring bags were placed over the end of pruned shoots at bud swell to initiate etiolation of the cuttings. The bags were left in place during shoot elongation to insure etiolation of the shoot base. Cuttings were harvested after 3 to 4 weeks, wounded, dipped in auxin, and placed on heating mats under an intermittent mist system. Rooting was evaluated on the cuttings after four weeks. Results showed the effects of etiolation to significantly increase the percentage of rooted cuttings and the number of roots per cutting

In-Place Randomized Slope-Selection

Slope selection is a well-known algorithmic tool used in the context of computing robust estimators for fitting a line to a collection $mathcal{P}$ of $n$ points in the plane. We demonstrate that it is possible to perform slope selection in expected $mathcal{O}(n log n)$ time using only constant extra space in addition to the space needed for representing the input. Our solution is based upon a space-efficient variant of Matouv{s}ek\u27s randomized interpolation search, and we believe that the techniques developed in this paper will prove helpful in the design of space-efficient randomized algorithms using samples. To underline this, we also sketch how to compute the repeated median line estimator in an in-place setting

Deterministic Sampling and Range Counting in Geometric Data Streams

We present memory-efficient deterministic algorithms for constructing epsilon-nets and epsilon-approximations of streams of geometric data. Unlike probabilistic approaches, these deterministic samples provide guaranteed bounds on their approximation factors. We show how our deterministic samples can be used to answer approximate online iceberg geometric queries on data streams. We use these techniques to approximate several robust statistics of geometric data streams, including Tukey depth, simplicial depth, regression depth, the Thiel-Sen estimator, and the least median of squares. Our algorithms use only a polylogarithmic amount of memory, provided the desired approximation factors are inverse-polylogarithmic. We also include a lower bound for non-iceberg geometric queries.Comment: 12 pages, 1 figur

An Optimal Algorithm for the Maximum-Density Segment Problem

We address a fundamental problem arising from analysis of biomolecular sequences. The input consists of two numbers $w_{\min}$ and $w_{\max}$ and a sequence $S$ of $n$ number pairs $(a_i,w_i)$ with $w_i>0$. Let {\em segment} $S(i,j)$ of $S$ be the consecutive subsequence of $S$ between indices $i$ and $j$. The {\em density} of $S(i,j)$ is $d(i,j)=(a_i+a_{i+1}+...+a_j)/(w_i+w_{i+1}+...+w_j)$. The {\em maximum-density segment problem} is to find a maximum-density segment over all segments $S(i,j)$ with $w_{\min}\leq w_i+w_{i+1}+...+w_j \leq w_{\max}$. The best previously known algorithm for the problem, due to Goldwasser, Kao, and Lu, runs in $O(n\log(w_{\max}-w_{\min}+1))$ time. In the present paper, we solve the problem in O(n) time. Our approach bypasses the complicated {\em right-skew decomposition}, introduced by Lin, Jiang, and Chao. As a result, our algorithm has the capability to process the input sequence in an online manner, which is an important feature for dealing with genome-scale sequences. Moreover, for a type of input sequences $S$ representable in $O(m)$ space, we show how to exploit the sparsity of $S$ and solve the maximum-density segment problem for $S$ in $O(m)$ time.Comment: 15 pages, 12 figures, an early version of this paper was presented at 11th Annual European Symposium on Algorithms (ESA 2003), Budapest, Hungary, September 15-20, 200

The Basis of Freezing Tolerance Between and Within Species Across Environmental Gradients with a Focus on Arctic, Alpine and Moorland Plants

Freezing events have devastating impacts on crops around the world. Climate change is resulting in more extreme freezing events as well as an increase in winter warm periods and shorter winters which can alter the process of acclimation and deacclimation leading to greater freezing susceptibility. Genes involved in freezing tolerance therefore need to be targeted by crop breeders to improve crop resistance to these events. The CBF family is one of these potential targets due to their presence across the Spermatophyta, including crop species, and their role in acclimation as transcription factors which activate cold response (COR) genes, thereby increasing freezing tolerance. Plants adapted to environments with frequent and very low temperature freezing events, such as arctic and alpine locations may, therefore, already possess modifications to these genes which improve freezing tolerance. The ability of native, dominant cover species to endure and adapt to these climatic changes can also be investigated via the study of variation within CBF over a species range. CBF sequences were isolated from numerous arctic and alpine species. Several common polymorphisms in key CBF regions were identified and applied to Arabidopsis thaliana CBF1. The effect upon freezing tolerance and CRT/DRE activation of these modified A. thaliana CBF1 sequences were then tested. No definitive conclusions could be drawn, however potential routes of further investigation are highlighted and discussed. CBF sequences of Empetrum nigrum samples from a wide distribution and both high and low altitude were compared, no differences between sequences which correlated with sample location, were found. However preliminary expression studies indicated a difference in the kinetics of CBF expression between samples from different locations. Further study of CBF expression kinetics within this species is highly recommended. Routes of further exploration leading to potential targets for crops are discussed, alongside suggested routes of further investigation for Empetrum nigrum and Calluna vulgaris