Life in a warm deep sea: routine activity and burst swimming performance of the shrimp Acanthephyra eximia in the abyssal Mediterranean

Measurements of routine swimming speed, "tail-flip'' escape responses, and oxygen consumptions were made of the deep-sea shrimp Acanthephyra eximia using autonomous landers in the Rhodos Basin at depths of up to 4,400 m and temperatures of 13 - 14.5 degrees C. Routine swimming speeds at 4,200 m averaged 0.18 m s(-1) or 3.09 body lengths s(-1), approximately double those of functionally similar oceanic scavengers. During escape responses peak accelerations of 23 m s(-2) or 630.6 body lengths s(-2) were recorded, with animals reaching speeds of 1.61 m s(-1) or 34.8 body lengths s(-2). When compared to shallow-water decapods at similar temperatures these values are low for a lightly calcified shrimp such as A. eximia despite a maximum muscle mass specific power output of 90.0 W kg(-1). A preliminary oxygen consumption measurement indicated similar rates to those of oceanic crustacean scavengers and shallower-living Mediterranean crustaceans once size and temperature had been taken into account. These animals appear to have high routine swimming speeds but low burst muscle performances. This suite of traits can be accounted for by high competition for limited resources in the eastern Mediterranean, but low selective pressure for burst swimming due to reductions in predator pressure

Planning and costing for the acceleration of actions for nutrition: experiences of countries in the Movement for Scaling Up Nutrition

This report is a synthesis of work undertaken by countries in the movement for Scaling Up Nutrition (SUN). The costed nutrition plans for 20 countries are analysed, looking at the assumptions made, the priority areas and targets which were set and the methods used, to determine whether they are responsive to the identified needs. It provides a basis for identifying priority areas for investment in each country, exploring answers to the questions - Are plans aligned with evidence-based recommendations? Is responsibility going beyond health sector? Which other sectors are engaged? What is the balance between specific nutrition interventions, nutrition-sensitive approaches and governance? What are the opportunities for non-Government stakeholders? Each country’s data is presented in a 2 page summary, outlining the scale of malnutrition, the distribution of programmes between specific, sensitive and governance and an outline of the costs, priorities and funding gaps. The plans themselves all have different strengths and weaknesses. This heterogeneity is inevitable, if only because countries have different interlinked sets of nutrition problems as this report clearly shows. But countries also have different sets of nutrition capacities. Importantly, the heterogeneity of the plans is a strength, not a weakness. As Lawrence Haddad concludes, “the plans serve as the most credible basis for investments to accelerate the reduction of undernutrition. They should be analysed, used, improved and backed

Algorithms on Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain

In this paper, we study the problem of moving $n$ sensors on a line to form a barrier coverage of a specified segment of the line such that the maximum moving distance of the sensors is minimized. Previously, it was an open question whether this problem on sensors with arbitrary sensing ranges is solvable in polynomial time. We settle this open question positively by giving an $O(n^2 \log n)$ time algorithm. For the special case when all sensors have the same-size sensing range, the previously best solution takes $O(n^2)$ time. We present an $O(n \log n)$ time algorithm for this case; further, if all sensors are initially located on the coverage segment, our algorithm takes $O(n)$ time. Also, we extend our techniques to the cycle version of the problem where the barrier coverage is for a simple cycle and the sensors are allowed to move only along the cycle. For sensors with the same-size sensing range, we solve the cycle version in $O(n)$ time, improving the previously best $O(n^2)$ time solution.Comment: This version corrected an error in the proof of Lemma 2 in the previous version and the version published in DCG 2013. Lemma 2 is for proving the correctness of an algorithm (see the footnote of Page 9 for why the previous proof is incorrect). Everything else of the paper does not change. All algorithms in the paper are exactly the same as before and their time complexities do not change eithe

On the Displacement for Covering a d−d-dimensional Cube with Randomly Placed Sensors

Consider $n$ sensors placed randomly and independently with the uniform distribution in a $d-$dimensional unit cube ($d\ge 2$). The sensors have identical sensing range equal to $r$, for some $r >0$. We are interested in moving the sensors from their initial positions to new positions so as to ensure that the $d-$dimensional unit cube is completely covered, i.e., every point in the $d-$dimensional cube is within the range of a sensor. If the $i$-th sensor is displaced a distance $d_i$, what is a displacement of minimum cost? As cost measure for the displacement of the team of sensors we consider the $a$-total movement defined as the sum $M_a:= \sum_{i=1}^n d_i^a$, for some constant $a>0$. We assume that $r$ and $n$ are chosen so as to allow full coverage of the $d-$dimensional unit cube and $a > 0$. The main contribution of the paper is to show the existence of a tradeoff between the $d-$dimensional cube, sensing radius and $a$-total movement. The main results can be summarized as follows for the case of the $d-$dimensional cube. If the $d-$dimensional cube sensing radius is $\frac{1}{2n^{1/d}}$ and $n=m^d$, for some $m\in N$, then we present an algorithm that uses $O\left(n^{1-\frac{a}{2d}}\right)$ total expected movement (see Algorithm 2 and Theorem 5). If the $d-$dimensional cube sensing radius is greater than $\frac{3^{3/d}}{(3^{1/d}-1)(3^{1/d}-1)}\frac{1}{2n^{1/d}}$ and $n$ is a natural number then the total expected movement is $O\left(n^{1-\frac{a}{2d}}\left(\frac{\ln n}{n}\right)^{\frac{a}{2d}}\right)$ (see Algorithm 3 and Theorem 7). In addition, we simulate Algorithm 2 and discuss the results of our simulations

Geometric Algorithms for Intervals and Related Problems

In this dissertation, we study several problems related to intervals and develop efficient algorithms for them. Interval problems have many applications in reality because many objects, values, and ranges are intervals in nature, such as time intervals, distances, line segments, probabilities, etc. Problems on intervals are gaining attention also because intervals are among the most basic geometric objects, and for the same reason, computational geometry techniques find useful for attacking these problems. Specifically, the problems we study in this dissertation includes the following: balanced splitting on weighted intervals, minimizing the movements of spreading points, dispersing points on intervals, multiple barrier coverage, and separating overlapped intervals on a line. We develop efficient algorithms for these problems and our results are either first known solutions or improve the previous work. In the problem of balanced splitting on weighted intervals, we are given a set of n intervals with non-negative weights on a line and an integer k ≥ 1. The goal is to find k points to partition the line into k + 1 segments, such that the maximum sum of the interval weights in these segments is minimized. We give an algorithm that solves the problem in O(n log n) time. Our second problem is on minimizing the movements of spreading points. In this problem, we are given a set of points on a line and we want to spread the points on the line so that the minimum pairwise distance of all points is no smaller than a given value δ. The objective is to minimize the maximum moving distance of all points. We solve the problem in O(n) time. We also solve the cycle version of the problem in linear time. For the third problem, we are given a set of n non-overlapping intervals on a line and we want to place a point on each interval so that the minimum pairwise distance of all points are maximized. We present an O(n) time algorithm for the problem. We also solve its cycle version in O(n) time. The fourth problem is on multiple barrier coverage, where we are given n sensors in the plane and m barriers (represented by intervals) on a line. The goal is to move the sensors onto the line to cover all the barriers such that the maximum moving distance of all sensors is minimized. Our algorithm for the problem runs in O(n2 log n log log n + nm log m) time. In a special case where the sensors are all initially on the line, our algorithm runs in O((n + m) log(n + m)) time. Finally, for the problem of separating overlapped intervals, we have a set of n intervals (possibly overlapped) on a line and we want to move them along the line so that no two intervals properly intersect. The objective is to minimize the maximum moving distance of all intervals. We propose an O(n log n) time algorithm for the problem. The algorithms and techniques developed in this dissertation are quite basic and fundamental, so they might be useful for solving other related problems on intervals as well