Fish Cohort Dynamics: Application of Complementary Modeling Approaches

The recruitment to the adult stock of a fish population is a function of both environmental conditions and the dynamics of juvenile fish cohorts. These dynamics can be quite complicated and involve the size structure of the cohort. Two types of models, i-state distribution models (e.g., partial differential equations) and i-state configuration models (computer simulation models following many individuals simultaneously), have been developed to study this type of question. However, these two model types have not to our knowledge previously been compared in detail. Analytical solutions are obtained for three partial differential equation models of early life-history fish cohorts. Equivalent individual-by-individual computer simulation models are also used. These two approaches can produce similar results, which suggests that one may be able to use the approaches interchangeably under many circumstances. Simple uncorrected stochasticity in daily growth is added to the individual-by-individual models, and it is shown that this produces no significant difference from purely deterministic situations. However, when the stochasticity was temporally correlated such that a fish growing faster than the mean 1 d has a tendency to grow faster than the mean the next day, there can be great differences in the outcomes of the simulations.This research was sponsored in part by the Electric Power Research Institute under contract no. RP2932-2 (DOE no. ERD-87-672) with the U.S. Department of Energy under contract no. DE-AC05-84OR21400 with Martin Marietta Energy Systems, and in part by grant no. NAI6RG0492-01 from the Coastal Ocean Program of the National Oceanic and Atmospheric Administration (NOAA) to the University of North Carolina Sea Grant College Program

Tanggapan Mahasiswa Pendidikan Biologi terhadap Pembelajaran Daring di Masa Pandemi Covid 19

Tujuan penelitian ini adalah untuk mengetahui tanggapan/persepsi mahasiswa Pendidikan Biologi terhadap pembelajaran daring yang dilaksanakan di masa karantina Pandemi Covid 19. Metode yang digunakan dalam penelitian ini adalah metode survei yang dilakukan secara online. Penggumpulan data primer dilakukan dengan menyebarkan kuisioner secara online kepada 328 responden (38% mahasiswa UNIKA, 44% mahasiswa UNDANA, 36% mahasiswa UMK dan 14% mahasiswa UNKRIS) yang  mengalami dampak pandemi Covid-19. Hasil temuan/penelitian menunjukkan bahwa mahasiswa umumnya mengikuti pembelajaran daring di rumah dengan menggunakan gadget (hp) melalui koneksi data pribadi dengan sinyal internet yang cukup baik (sedang). Mayoritas mahasiswa lebih suka menggunakan aplikasi elearning dan Gmeet  ketika daring dengan bentuk paket materi berupa power point (PPT) dan  substansi materi yang sesuai/ relevan dengan kompetensi yang harus dikuasai.  Program Pembelajaran Daring ini berkontribusi pada peningkatan keunggulan akademik (academic excellent),manfaat ekonomi (economic benefit), dan dampak sosialnya (social impact) seperti fleksibilitas tempat dan waktu sehingga memudahkan masyarakat untuk mengikuti pembelajaran kapan pun dan di manapun, namun perkuliahan Daring selama masa pandemi covid 19 dinilai kurang efektif karena mahasiswa mengalami Kendala seperti banyaknya tugas, perkuliahan tidak on time, diskusi yang monoton dan bahan/materi yang diberikan terbatas

A heuristic approach for new-item cold start problem in recommendation of micro open education resources

© Springer International Publishing AG, part of Springer Nature 2018. The recommendation of micro Open Education Resources (OERs) suffers from the new-item cold start problem because little is known about the continuously published micro OERs. This paper provides a heuristic approach to inserting newly published micro OERs into established learning paths, to enhance the possibilities of new items to be discovered and appear in the recommendation lists. It considers the accumulation and attenuation of user interests and conform with the demand of fast response in online computation. Performance of this approach has been proved by empirical studies

Alleviating the new user problem in collaborative filtering by exploiting personality information

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-zThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3-40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.This work was supported by the Spanish Ministry of Economy and Competitiveness (TIN2013-47090-C3). We thank Michal Kosinski and David Stillwell for their attention regarding the dataset

Increasing Costs Due to Ocean Acidification Drives Phytoplankton to Be More Heavily Calcified: Optimal Growth Strategy of Coccolithophores

Ocean acidification is potentially one of the greatest threats to marine ecosystems and global carbon cycling. Amongst calcifying organisms, coccolithophores have received special attention because their calcite precipitation plays a significant role in alkalinity flux to the deep ocean (i.e., inorganic carbon pump). Currently, empirical effort is devoted to evaluating the plastic responses to acidification, but evolutionary considerations are missing from this approach. We thus constructed an optimality model to evaluate the evolutionary response of coccolithophorid life history, assuming that their exoskeleton (coccolith) serves to reduce the instantaneous mortality rates. Our model predicted that natural selection favors constructing more heavily calcified exoskeleton in response to increased acidification-driven costs. This counter-intuitive response occurs because the fitness benefit of choosing a better-defended, slower growth strategy in more acidic conditions, outweighs that of accelerating the cell cycle, as this occurs by producing less calcified exoskeleton. Contrary to the widely held belief, the evolutionarily optimized population can precipitate larger amounts of CaCO3 during the bloom in more acidified seawater, depending on parameter values. These findings suggest that ocean acidification may enhance the calcification rates of marine organisms as an adaptive response, possibly accompanied by higher carbon fixation ability. Our theory also provides a compelling explanation for the multispecific fossil time-series record from ∼200 years ago to present, in which mean coccolith size has increased along with rising atmospheric CO2 concentration

Facing the cold start problem in recommender systems

A recommender system (RS) aims to provide personalized recommendations to users for specific items (e.g., music, books). Popular techniques involve content-based (CB) models and collaborative filtering (CF) approaches. In this paper, we deal with a very important problem in RSs: The cold start problem. This problem is related to recommendations for novel users or new items. In case of new users, the system does not have information about their preferences in order to make recommendations. We propose a model where widely known classification algorithms in combination with similarity techniques and prediction mechanisms provide the necessary means for retrieving recommendations. The proposed approach incorporates classification methods in a pure CF system while the use of demographic data help for the identification of other users with similar behavior. Our experiments show the performance of the proposed system through a large number of experiments. We adopt the widely known dataset provided by the GroupLens research group. We reveal the advantages of the proposed solution by providing satisfactory numerical results in different experimental scenarios. © 2013 Elsevier Ltd. All rights reserved