Estimation of Sorting Time for Arthropod Samples Collected with Tullgren Funnels

Arthropods were sorted from samples obtained with Tullgren funnels. Each sorter maintained a log of time per session and arthropods removed per session. Five individuals removed all arthropods from 12 separate samples and sorted them into previously designated class or ordinal taxa. Each sample was sorted by a single student. Students were allowed to develop their own approaches to sorting and do it as time permitted. Mean sorting rate per sample was 2.43 arthropods per minute, with a range of 1.42-5.64, while mean sorting rate for a sorting session was 3.41 specimens per minute. Specimen density was only weakly correlated with sort time. Fatigue did not appear to be a major factor in sorting rate, as indicated by the similarity of the linear and quadratic coefficients of determination for each sample

Engineering Faster Sorters for Small Sets of Items

Sorting a set of items is a task that can be useful by itself or as a building block for more complex operations. That is why a lot of effort has been put into finding sorting algorithms that sort large sets as fast as possible. But the more sophisticated and complex the algorithms become, the less efficient they are for small sets of items due to large constant factors. We aim to determine if there is a faster way than insertion sort to sort small sets of items to provide a more efficient base case sorter. We looked at sorting networks, at how they can improve the speed of sorting few elements, and how to implement them in an efficient manner by using conditional moves. Since sorting networks need to be implemented explicitly for each set size, providing networks for larger sizes becomes less efficient due to increased code sizes. To also enable the sorting of slightly larger base cases, we adapted sample sort to Register Sample Sort, to break down those larger sets into sizes that can in turn be sorted by sorting networks. From our experiments we found that when sorting only small sets, the sorting networks outperform insertion sort by a factor of at least 1.76 for any array size between six and sixteen, and by a factor of 2.72 on average across all machines and array sizes. When integrating sorting networks as a base case sorter into Quicksort, we achieved far less performance improvements, which is probably due to the networks having a larger code size and cluttering the L1 instruction cache. But for x86 machines with a larger L1 instruction cache of 64 KiB or more, we obtained speedups of 12.7% when using sorting networks as a base case sorter in std::sort. In conclusion, the desired improvement in speed could only be achieved under special circumstances, but the results clearly show the potential of using conditional moves in the field of sorting algorithms.Comment: arXiv admin note: substantial text overlap with arXiv:1908.0811

B746: Economies of Size for Maine Potato Packing Plants

The objective of this study was to find where economies in scale lie, and what, if any, would be the preferred or most economical in packing facilities. Data relative to equipment and labor requirements and capabilities and to materials and services were obtained through manufacturers, sales agencies, research studies and case studies. Two computerized programs were developed to select equipment, labor, and facilities which would be most efficient and least-cost and this was done for packing 10-pound bags with potatoes. Ten model lines resulted from the analysis allowing for input rates of 80 to 800 cwt/hr when based on a particular set of variables established as standard . Among the standard variables were 180 working days of 9 hours each, with the equipment operating 80% of the time handling an input flow of potatoes from which 10 percent were removed as undersized and 10 percent were removed as grade defective.https://digitalcommons.library.umaine.edu/aes_bulletin/1098/thumbnail.jp

End-of-pipe Waste Analysis and Integrated Solid Waste Management Plan

A ten-year integrated solid waste management plan was established for the University of the Philippines Los Baños which complies with the provisions of RA 9003. An end-of-pipe Waste Analysis and Characterization Study (WACS) was performed to identify the classification of wastes in UPLB. Waste generation was found to be 593.67 kg/day on the average and is expected to increase by 2% per year which is 709.49 kg/day on the year 2027. The waste composition by weight of the non-biodegradable wastes are as follows: plastic (55.68%); paper (35.77%); glass bottles (5.22%); metal (2.77%); and residuals (0.55%). A large portion of the wastes, which is 99.45% by weight, are recyclables.  The loose density of wastes is 131.93 kg/ m3. Feasible collection points were assigned to improve efficiency of the collection of wastes in the university. Building units inside the campus were clustered and was assigned to dispose wastes to a single temporary storage facility per cluster. There are 181 units of 240-L garbage bin needed for the 39 clusters in UPLB. Two sets of dimensions of a proposed temporary storage facility were provided for the temporary storage facility; 5 2 2.2 m and 3.5 2 2.2 m. Conceptual design and structural plans of the materials recovery facility were provided. Mass balance was performed, and the theoretical diversion efficiency of the materials recovery facility is 99.445%