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3,139 results for “fitting”
Data from: Expression of additive genetic variance for fitness in a population of partridge pea in two field sites
Despite the importance of adaptation in shaping biological diversity over many generations, little is known about populations' capacities to adapt at any particular time. Theory predicts that a population's rate of ongoing adaptation is the ratio of its additive genetic variance for fitness, VA (W), to its mean absolute fitness, W̅. We conducted a transplant study to quantify W̅ and standing VA (W) for a population of the annual legume Chamaecrista fasciculata in one field site from which we initially sampled it and another site where it does not currently occur naturally. We also examined genotype‐by‐environment interactions, G x E, as well as its components, differences between sites in VA (W) and in rank of breeding values for fitness. The mean fitness indicated population persistence in both sites, and there was substantial VA (W) for ongoing adaptation at both sites. Statistically significant G x E indicated that the adaptive process would differ between sites. We found a positive correlation between fitness of genotypes in the "home" and "away" environments, and G x E was more pronounced as the life cycle proceeds. This study exemplifies an approach to assessing whether there is sufficient VA (W) to support evolutionary rescue in populations that are declining.
Data from: The demography of a resource specialist in the tropics: Cecropia trees and the fitness of three-toed sloths
Resource specialists persist on a narrow range of resources. Consequently, the abundance of key resources should drive vital rates, individual fitness and population viability. While Neotropical forests feature both high levels of biodiversity and numbers of specialist species, no studies have directly evaluated how the variation of key resources affects the fitness of a tropical specialist. Here, we quantified the effect of key tree species density and forest cover on the fitness of three-toed sloths (Bradypus variegatus), an arboreal folivore strongly associated with Cecropia trees, in Costa Rica using a multi-year demographic, genetic and space use dataset. We found that the density of Cecropia trees was strongly and positively related to both adult survival and reproductive output. A matrix model parameterized with Cecropia-demography relationships suggested positive growth of sloth populations, even at low densities of Cecropia (0.7 trees/ha). Our study shows the first direct link between the density of a key resource to demographic consequences of a tropical specialist, underscoring the sensitivity of tropical specialists to the loss of a single key resource, but also point to targeted conservation measures to increase that resource. Finally, our study reveals that previously disturbed and regenerating environments can support viable populations of tropical specialists.
Data from: Chemical defense over decadal scales: ontogenetic allocation trajectories and consequences for fitness in a foundation tree species
1. Expression of herbivore defense traits can change dramatically during the course of plant development. Little is known, however, about the degree of genetic or sexual variation in these ontogenetic defense trajectories or whether the trajectories themselves are adaptive, especially in long‐lived species. 2. We used a 13‐year dataset of chemical defense traits, growth, and survivorship from a common garden of trembling aspen (Populus tremuloides) genotypes to document long‐term defense trajectories and their relationship to tree fitness during juvenile and early mature stages. 3. Overall, concentrations of the two principal classes of aspen defense compounds (salicinoid phenolic glycosides [SPGs] and condensed tannins [CTs]) decreased to differing degrees in foliage of juvenile trees and then remained relatively constant in maturity. Initial values, juvenile rates of change, and average mature values all exhibited significant genetic variation for both SPGs and CTs. 4. Relationships between defense trajectory parameters and metrics of tree fitness (growth and survivorship) depended on compound type and tree sex. Females with higher‐allocation SPG trajectories (high initial juvenile concentrations, slow juvenile declines, high mature concentrations) grew more slowly relative to females with lower‐allocation trajectories. In males, higher‐allocation SPG trajectories had a lesser effect on growth but were associated with reduced mortality. Juvenile CT trajectories were not correlated with tree fitness, but average CT concentration in maturity was positively related to growth in females. 5. These results suggest that ontogenetic defense trajectories are adaptive and subject to natural selection. Genotypic variation and ontogeny shape tree defensive chemistry, both independently and interactively. These patterns of defense expression have the potential to structure trophic interactions and the genetic composition of forests in both space and time.
Data from: Direct fitness benefits and kinship of social foraging groups in an Old World tropical babbler
Molecular studies have revealed that social groups composed mainly of non-relatives may be widespread in group-living vertebrates, but the benefits favoring such sociality are not well understood. In the Old World, birds often form conspecific foraging groups that are maintained year-round and offspring usually disperse to other social groups. We tested the hypothesis that non-breeding group members are largely unrelated and gain direct fitness benefits through breeding opportunities (males) and brood parasitism (females) in the tropical grey-throated babbler, Stachyris nigriceps, in Malaysian Borneo. Babblers foraged in social groups containing one or more breeding pairs (median = 8 group members of equal sex ratio), but group members rarely assisted with breeding (9% of 67 breeding pairs had a third helper; exhibiting facultative cooperative breeding). Although 20% of 266 group member dyads were first-order relatives of one or both members of the breeding pairs, 80% were unrelated. Male group members gained direct fitness benefits through extra-pair and extra-group paternity (25% of 73 offspring), which was independent of their relatedness to the breeding pair and increased with decreasing group size. In contrast, females did not gain direct fitness benefits through brood parasitism. The low levels of relatedness and helping in social groups suggest that most group members do not gain indirect fitness benefits by helping to raise unrelated offspring. These findings highlight the importance of examining benefits of sociality for unrelated individuals that largely do not help and broaden the direct fitness benefits of group foraging beyond assumed survival benefits.
Data from: Select and resequence reveals relative fitness of bacteria in symbiotic and free-living environments
Assays to accurately estimate relative fitness of bacteria growing in multistrain communities can advance our understanding of how selection shapes diversity within a lineage. Here, we present a variant of the "evolve and resequence" approach both to estimate relative fitness and to identify genetic variants responsible for fitness variation of symbiotic bacteria in free-living and host environments. We demonstrate the utility of this approach by characterizing selection by two plant hosts and in two free-living environments (sterilized soil and liquid media) acting on synthetic communities of the facultatively symbiotic bacterium Ensifer meliloti. We find (i) selection that hosts exert on rhizobial communities depends on competition among strains, (ii) selection is stronger inside hosts than in either free-living environment, and (iii) a positive host-dependent relationship between relative strain fitness in multistrain communities and host benefits provided by strains in single-strain experiments. The greatest changes in allele frequencies in response to plant hosts are in genes associated with motility, regulation of nitrogen fixation, and host/rhizobia signaling. The approach we present provides a powerful complement to experimental evolution and forward genetic screens for characterizing selection in bacterial populations, identifying gene function, and surveying the functional importance of naturally occurring genomic variation.
Wmid-f3dde6 - probable strap fitting
An incomplete probable strap fitting or possible shoe cleat, of Post Medieval dating (AD 1600 to AD 1800). The probable strap fitting is rectangular in shape, with a rectangular slot in the centre. This measures 13.2 mm in length and 4.9 mm wide.The short edges curve round to form downward pointing points (both incomplete). Traces of iron corrosion products are present at either end of the rectangular slot. Rectangular recesses are present on both long edges. No decoration is visible on the upper surface. For further information, please visit the database record at: https://finds.org.uk/database/artefacts/record/id/1055819 Source: Objaverse 1.0 / Sketchfab
A set of drinking horn fittings and a chain No.1
Metal elements of the equipment from the so-called ducal tomb: A set of drinking horn fittings and a chain No.1 ID no.: SZA7/ 00/704, SZA7 / 00/705, SZA7 / 00/706 Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/1573 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Wmid-c22361 - Post Medieval strap fitting
A complete copper alloy strap fitting of uncertain dating but probably Post Medieval (AD 1600 to AD 1800). The strap fitting is rectangular in shape. It has been made from a folded piece of copper alloy metal, then bent in half to form a loop shape and secured at the top with a copper alloy rivet. The rivet head has a diameter of 10.1 mm and the shank has a diameter of 6.6 mm. The sheet copper alloy metal has a thickness of 0.6 mm. No decoration is present on either side. For more information, please visit the online database record available at: https://finds.org.uk/database/artefacts/record/id/1070998 Source: Objaverse 1.0 / Sketchfab
Data from The Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration
<p>Landscape data for "The Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration", G. Ochoa, N. Veerapen, D. Whitley and E. Burke. Artificial Evolution (EA 2015), 26-28 October 2015, Lyon, France.</p>
Data used to produce figures in "Monotonicity of Fitness Landscapes and Mutation Rate Control"
<p>Data used in Figures 2, 3, 4, 6, 7, 8, 9 and 10 of the manuscript "Monotonicity of Fitness Landscapes and Mutation Rate Control"</p>
Dataset for article: Luque-Casado, A., Perakakis, P., Ciria, L.F., Sanabria, D. (2016). Transient autonomic responses during sustained attention in high and low fit young adults. Sci. Rep. 6, 27556
<p>Dataset for article:</p> <p>Luque-Casado, A., Perakakis, P., Ciria, L.F., Sanabria, D. (2016). Transient autonomic responses during sustained attention in high and low fit young adults. Sci. Rep. 6, 27556</p>
Quasar spectra and absorption profile fits of HE 0515-4414 for limiting fine-structure constant variability
<p>This is the first public pre-release upon submission to MNRAS and to obtain a DOI.</p>
Data and code for "Competition contributes to quantitative mismatches between plant fitness and occurrence along environmental gradients"
<p>This repository contains data and code for the following manuscript: Hayashi, K. T., & Kraft, N. J. B. (2025). Competition contributes to quantitative mismatches between plant fitness and occurrence along environmental gradients. Journal of Ecology, 113, 2590–2602. <a href="https://doi.org/10.1111/1365-2745.70115">https://doi.org/10.1111/1365-2745.70115</a></p>
The phenotypic and fitness response to the combination of copper and thermal stressors strongly varies within the ciliate species, Tetrahymena thermophila
<p><span>Copper pollution can alter biological and trophic functions. Organisms can set up different tolerance strategies, including accumulation mechanisms (intracellular vacuoles, external chelation, etc.) to maintain themselves in copper-polluted environments. Accumulation mechanisms can influence the expression of other phenotypic traits, allowing organisms to improve their fitness. Whether copper effects on accumulation strategies interact with other environmental stressors such as temperature and how this may differ within species are still unsolved questions. Here, we tested experimentally whether the combined effect of copper and temperature modulates traits linked to fitness, morphology, movement and accumulation in six strains of the ciliate <em>Tetrahymena</em> <em>thermophila</em>. We also explored whether copper accumulation might modulate environmental copper concentration effects on phenotypic and fitness traits. Results showed high intraspecific variability in the phenotypic and fitness response to copper, with interactive effects between temperature and copper. In addition, they suggested an attenuation effect of copper accumulation on the sensitivity of traits to copper, but with great variation between strains, temperature and copper concentration. Diversity of responses among strains and their thermal dependencies pleads for the integration of intraspecific variability and multiple stressors approaches in ecotoxicological studies, thus improving the reliability of assessments of the effects of pollutants on biodiversity.</span></p>
Experiments with Frequency Fitness Assignment based Algorithms on the Traveling Salesperson Problem
<p><strong>1. Introduction</strong></p><p>In this archive, we provide the implementation and experimental results of eight different algorithms to solve Traveling Salesperson Problem (TSP) instances from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>.</p><p>A TSP is defined by a fully-connected weighted graph of n cities. The goal is to find the overall shortest tour that visits each cities exactly once and returns to its starting point. The TSP is NP-hard. We consider 56 symmetric instances from the well-known <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>.</p><p>Solutions in our work are stored in the path representation, where such a tour is encoded as a permutation x of the numbers 1 to n, each identifying a city. If a city appears at index j in the permutation x, then it will be the jth city to be visited. This means that a tour x will pass the following edges: (x[1], x[2]), (x[2], x[3]), (x[3], x[4]), … (x[n-1], x[n]), (x[n], x[1]).</p><p><strong>2. Directory Structure</strong></p><p>This dataset is split into multiple separate <i>tar.xz</i> archives. These can be unpacked in the same folder and will produce the directory structure described below. Each archive contains this note and the license information, but apart from that, there is no redundancy.</p><p>This archive contains the following directories:</p><ul><li>source contains the Python source codes needed to run the experiment.<ul><li>moptipy-main is a local copy of the <a href="https://thomasweise.github.io/moptipy">moptipy</a> package used for our experiment.</li><li>tsplib contains the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> data. This includes the instances used in our experiments as files in text format with suffix .tsp. If an optimal tour is given by TSPLib, it is stored in a text format file with suffix .opt.tour and name prefix identical to the instance file. In other words, the file eil51.tsp contains the TSP instance eil51 and the file eil51.opt.tour contains the corresponding optimal tour. Both the TSP instances and optimal tours can be downloaded from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/</a>. We also include the documentation of TSPLIB in file <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">tsp95.pdf</a> documenting them. We further include the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/TSPFAQ.html">TSPLIB FAQ</a> both as HTML and PDF file (tsplib_faq.html and tsplib_faq.pdf) and the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/STSP.html">list of known optimal tour lengths</a> as HTML and PDF file (optimal_tour_lengths_of_symmetric_tsps.html, optimal_tour_lengths_of_symmetric_tsps.pdf). Notice that, while the TSP instances we used are Euclidean, all distances are converted to integers as prescribed by the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">documentation</a>.</li></ul></li><li>results is the directory with the log files. Each log file contains information of one run, i.e., one execution of one algorithm on one problem instance. All improving moves of a run as well as the final solution are stored in the log file. The direct sub-folders results represent the algorithms and contain one folder per TSP instance, which, in turn, contain the log files.</li><li>evaluation is a folder with the extracted evaluation and figures</li><li>evaluation_edited is a folder with evaluation figures slightly edited for better visual appeal (but obviously without changing any result / scientific content)</li><li>evaluator is a folder with a Python script main.py that generates all the files in evaluation from the data it finds in results. It requires the <a href="https://thomasweise.github.io/moptipy">moptipy</a> package being installed for running in the version given in requirements.txt.</li></ul><p><strong>3. Algorithms</strong></p><p>The (1+1) EA is the most basic evolutionary algorithm and also be considered as a randomized local search. It starts with one random solution/permutation xc and computes its length yc=f(xc). In each iteration, it applies a unary search operator op to obtain a new tour xn=op(xc) and computes its length yn=f(xn). If yn<=yc, then it will accept the new tour and set xn=xn and yc=yn. The results of this algorithm are given in folder results/ea_revn.</p><p>FFA is a fitness assignment process that takes place before this last step in the EA. We integrate FFA into the (1+1) EA and obtain the (1+1) FEA. This algorithm uses an additional table H which counts, for any tour length y, how often it has been seen during the search so far. After the new tour xn is created and its objective value yn is computed, the (1+1) FEA sets H[yc] = H[yc] + 1 and H[yn] = H[yn] + 1. It will accept xn if and only if H[yn] <= H[yc] and, only in this case, set xn=xn and yc=yn. The results of this algorithm are given in folder results/fea_revn.</p><p>SA is the classical simulated annealing algorithm. In our experiment, it will accept the new solution xn with probability P. If the new solution is better, the acceptance probability P is 1. For worse solutions, the probability is between 0 and 1, i.e., sometimes, worse solution are also accepted. This algorithm has a temperature cooling schedule. It starts at an initial temperature and over time, the temperature decreases. The probability P of accepting the worse solution depends on the temperature and decreases as well. The results of this algorithm are given in folder results/sa_revn.</p><p>An FFA-based version of SA uses the frequency fitness instead of the objective values in all acceptance decisions. The results of this algorithm are given in folder results/fsa_revn.</p><p>EAFEA(A) is a hybrid which alternates between the EA and the FEA and copies a solution from the FEA to the EA if it has an entirely new objective value, i.e., if H[yn] = 1. The results of this algorithm are given in folder results/eafea2_revn.</p><p>SAFEA(A) is a hybrid which alternates between the SA and the FEA and copies a solution from the FEA to the SA if it has an entirely new objective value, i.e., if H[yn] = 1. The results of this algorithm are given in folder results/safea2_revn.</p><p>EAFEA(B) is a hybrid which alternates between the EA and the FEA and copies a solution from the FEA to the EA part if it has a better objective value. The results of this algorithm are given in folder results/eafea_revn.</p><p>SAFEA(B) is a hybrid which alternates between the SA and the FEA and copies a solution from the FEA to the SA part if it has a better objective value. The results of this algorithm are given in folder results/safea_revn.</p><p>We apply all algorithms with the same unary operator reverse, which reverses a randomly chosen subsequence of the tour. This operator is also often called a "2-opt move". It has the advantage that the new objective value of a new solution can be computed in O(1) if the objective value of the solution from which it is derived is known.</p><p><strong>4. How to Run the Experiment</strong></p><p>First, you need to make sure to have all the dependencies installed that this program requires. You can do this by executing the following command in the terminal:</p><p>pip install matplotlib numba numpy psutil scikit-learn moptipy moptipyapps</p><p>Now enter the source directory, i.e., the directory containing the run.py file, in your terminal. Depending on your system configuration and whether you run Windows or Linux, you can start the program with <i>one</i> of the commands below. (If running the first command returns with an error, just try the next one in the list.)</p><ul><li>python3 -m run</li><li>python -m run</li><li>python run.py</li><li>python3 run.py</li></ul><p>Then the experiment will run. It will automatically create a sub-folder results in source and place all log files that are generated into it. Be careful: The experiment will take a long time. However, if you have multiple CPUs, you can simply start several instances of this program in independent terminals. Each instance will then conduct different runs. This also works if this folder is shared over the network, in which case you can run multiple processes on multiple PCs.</p><p>Side note: This experiment uses the <a href="https://thomasweise.github.io/moptipy">moptipy</a> package for implementing its algorithms, running the experiments, and gathering their results. If you want to install moptipy on your system instead of using the version supplied here, you can install it via pip install moptipy. It also uses moptipyapps to load some data.</p><p><strong>5. Literature</strong></p><ul><li>Frequency Fitness Assignment (FFA):<ol><li>Thomas Weise, Zhize Wu, Xinlu Li, Yan Chen, and Jörg Lässig. Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient. IEEE Transactions on Evolutionary Computation (TEVC). 2022. Early Access. doi:<a href="https:doi.org/10.1109/TEVC.2022.3191698">10.1109/TEVC.2022.3191698</a>.</li><li>Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value. <i>IEEE Transactions on Evolutionary Computation</i> 25(2):307–319. April 2021. Preprint available at <a href="http://arxiv.org/abs/2001.01416">arXiv:2001.01416v5</a> [cs.NE] 15 Oct 2020. doi:<a href="http://dx.doi.org/10.1109/TEVC.2020.3032090">10.1109/TEVC.2020.3032090</a>. Experimental results and source code are available at doi:<a href="http://doi.org/10.5281/zenodo.3899474">10.5281/zenodo.3899474</a>.</li><li>Tianyu Liang, Zhize Wu, Jörg Lässig, Daan van den Berg, and Thomas Weise. Solving the Traveling Salesperson Problem using Frequency Fitness Assignment. In Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, and Keeley A. Crockett, editors, Proceedings of the IEEE Symposium on Foundations of Computational Intelligence (IEEE FOCI'22), part of the IEEE Symposium Series on Computational Intelligence (SSCI 2022). December 4–7, 2022, Singapore, pages 360–367. IEEE. doi:<a href="https://doi.org/10.1109/SSCI51031.2022.10022296">10.1109/SSCI51031.2022.10022296</a>.</li><li>Thomas Weise, Mingxu Wan, Ke Tang, Pu Wang, Alexandre Devert, and Xin Yao. Frequency Fitness Assignment. <i>IEEE Transactions on Evolutionary Computation (IEEE-EC)</i> 18(2):226-243, April 2014. doi:<a href="http://dx.doi.org/10.1109/TEVC.2013.2251885">10.1109/TEVC.2013.2251885</a>.</li><li>Thomas Weise, Xinlu Li, Yan Chen, and Zhize Wu. Solving Job Shop Scheduling Problems Without Using a Bias for Good Solutions. In <i>Genetic and Evolutionary Computation Conference Companion (GECCO'21 Companion),</i> July 10-14, 2021, Lille, France. ACM, New York, NY, USA. ISBN 978-1-4503-8351-6. doi:<a href="http://doi.org/10.1145/3449726.3463124">10.1145/3449726.3463124</a>.</li><li>Thomas Weise, Yan Chen, Xinlu Li, and Zhize Wu. Selecting a diverse set of benchmark instances from a tunable model problem for black-box discrete optimization algorithms. <i>Applied Soft Computing Journal (ASOC)</i>, 92:106269, June 2020. doi:<a href="http://dx.doi.org/10.1016/j.asoc.2020.106269">10.1016/j.asoc.2020.106269</a>.</li><li>Thomas Weise, Mingxu Wan, Ke Tang, and Xin Yao. Evolving Exact Integer Algorithms with Genetic Programming. In <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC'14), Proceedings of the 2014 World Congress on Computational Intelligence (WCCI'14)</i>, pages 1816-1823, Beijing, China, July 6-11, 2014. Los Alamitos, CA, USA: IEEE Computer Society Press. ISBN: 978-1-4799-1488-3. doi:<a href="http://dx.doi.org/10.1109/CEC.2014.6900292">10.1109/CEC.2014.6900292</a>.</li></ol></li><li>Traveling Salesperson Problem (TSP):<ol><li>Pedro Larrañaga, Cindy M. H. Kuijpers, Roberto H. Murga, I. Inza, and S. Dizdarevic. Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators. <i>Artificial Intelligence Review,</i> 13(2):129–170, April 1999. Kluwer Academic Publishers, The Netherlands. doi:<a href="https://doi.org/10.1023/A:1006529012972">10.1023/A:1006529012972</a>.</li><li>Gerhard Reinelt. TSPLIB — A Traveling Salesman Problem Library. <i>ORSA Journal on Computing</i> 3(4):376-384. 1991. <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/</a>.</li><li>Gerhard Reinelt. TSPLIB95. 1995. Heidelberg, Germany: Universität Heidelberg, Institut für Angewandte Mathematik. <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf</a>.</li><li>Thomas Weise, Raymond Chiong, Ke Tang, Jörg Lässig, Shigeyoshi Tsutsui, Wenxiang Chen, Zbigniew Michalewicz, and Xin Yao. Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem. <i>IEEE Computational Intelligence Magazine (CIM)</i> 9(3):40-52, August 2014. doi:<a href="http://dx.doi.org/10.1109/MCI.2014.2326101">10.1109/MCI.2014.2326101</a>.</li><li>Eugene Leighton Lawler, Jan Karel Lenstra, Alexander Hendrik George Rinnooy Kan, and David B. Shmoys. <i>The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization.</i> Wiley Interscience. 1985.</li><li>David Lee Applegate, Robert E. Bixby, Vasek Chvatal, and William John Cook. <i>The Traveling Salesman Problem: A Computational Study.</i> Princeton University Press. 2007.</li><li>Gregory Z. Gutin and Abraham P. Punnen, editors. <i>The Traveling Salesman Problem and its Variations.</i> Volume 12 of Combinatorial Optimization. Kluwer Academic Publishers. 2002. doi:<a href="https://dx.doi.org/10.1007/b101971">10.1007/b101971</a>.</li></ol></li><li>Software:<ol><li>The Metaheuristic Optimization in Python Package <a href="https://thomasweise.github.io/moptipy">moptipy</a></li></ol></li></ul><p><strong>6. License</strong></p><p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the files of <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> in directory source/tsplib, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file LICENSE.txt in this archive.</p><p><strong>7. Contact</strong></p><p>If you have any questions or suggestions, please contact</p><p>Mr. Tianyu LIANG (梁天宇) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥学院</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:liangty@stu.hfuu.edu.cn">liangty@stu.hfuu.edu.cn</a>.</p>
Data from: Fitness surfaces and local thermal adaptation in Drosophila along a latitudinal gradient
<p><span>Local adaptation is commonly cited to explain species distribution, but how fitness varies along continuous geographical gradients is not well understood. Here we combine thermal biology and life-history theory to demonstrate that <em>Drosophila </em>populations along a 2,500 km latitudinal cline are adapted to local conditions. We measured how heat tolerance and viability rate across 8 populations vary with temperature in the laboratory, and then simulated their expected cumulative Darwinian fitness employing high-resolution temperature data from their 8 collection sites. Simulations indicate a trade-off between annual survival and cumulative viability, as both mortality and the recruitment of new flies are predicted to increase in warmer regions. Importantly, populations are locally adapted and exhibit the optimal combination of both traits to maximize fitness where they live. In conclusion, our method is able to reconstruct fitness surfaces employing empirical life-history estimates and reconstructs peaks representing locally adapted populations, allowing to study geographic adaptation <em>in silico</em>.</span></p>
Decorated fitting from the iron age.
Decorated fitting from the iron age. Made from cobber alloy (bronze or brass) and gold. Diameter 3,7cm. Found with metal detector by Vidar Fjelde Havn. Museum ID: SS14131 Found in: Strand - Rogaland - Norway Found by : Vidar Fjelde Havn in 2018. Source: Objaverse 1.0 / Sketchfab
Wmid-c46f25 - Post medieval furniture fitting
An incomplete copper alloy modern zoomorphic vessel or furniture foot of Modern dating (AD 1800 to AD 1950). The object takes the form of a three-toed, clawed birds foot. A flat plate projects at a right-angle from the angle, terminating in a split break where the edges of a circular attachment hole is visible. This hole has an internal diameter of 2.8 mm. Two supportive knops project from the centre and rear edge of the base. For more information, please visit the online database record available at: https://finds.org.uk/database/artefacts/record/id/1071077 Source: Objaverse 1.0 / Sketchfab
Wmid-046fb1 - strap fitting
A complete copper alloy strap fitting or belt mount, of medieval date (13th to 15th Centuries). The mount is of the bar type and consists of a domed central oval, with two projecting lugs. The projecting lugs are rectangular in shape, with rounded terminals, with a central perforation. The holes are recessed on the rear of the mount. Both holes still have part of the copper alloy attachment rivets present. The central oval is domed, with five horizontal grooves as decoration in the middle. The reverse is concave. The reverse is undecorated. For further information, please visit the online database record available at: https://finds.org.uk/database/artefacts/record/id/1069812 Source: Objaverse 1.0 / Sketchfab
073 Gürtelbeschlag / Belt fitting
# Fundort / Site Brilon-Alme, Hochsauerlandkreis (Germany) Wüstung Walberinchusen / Abandoned village Walberinchusen # Fundumstände / Circumstances of discovery Einzelfund / Single find 2004 # Objekt / Object Buntmetall (vergoldet und versilbert) / Non-ferrous metal (gold-plated and silver-plated) Länge / Length: 9,2 cm Breite / Width: 2,65 cm # Datierung / Dating 9. Jahrhundert / 9th century Frühmittelalter / Early Middle Ages Karolinger / Carolingians # Fundverbleib / Repository Zentrales Fundarchiv der LWL-Archäologie für Westfalen, Münster # 3-D-Modell / 3D model Fotokamera / Photo camera: Nikon D850 Modellberechnung / Model processing: Reality Capture Modell / Model: LWL-Archäologie für Westfalen/Florian Westphal Source: Objaverse 1.0 / Sketchfab
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