A lot-sizing problem in deliberated and controlled co-production systems

We consider an uncapacitated lot sizing problem in co-production systems, in which it is possible to produce multiple items simultaneously in a single production run. Each product has a deterministic demand to be satisfied on time. The decision is to choose which items to co-produce and the amount of production throughout a predetermined planning horizon. We show that the lot sizing problem with co-production is strongly NP-Hard. Then, we develop various mixed-integer linear programming (MILP) formulation of the problem and show that LP relaxations of all MILPs are equal. We develop a separation algorithm based on a set of valid inequalities, lower bounds based on a dynamic lot-sizing relaxation of our problem and a constructive heuristic that is used to obtain an initial solution for the solver, which form the basis of our proposed Branch & Cut algorithm for the problem. We test our models and algorithms on different data sets and provide the results.WOS:000754103800001Scopus - Affiliation ID: 60105072Science Citation Index ExpandedQ2-Q3Article; Early AccessUluslararası işbirliği ile yapılan - HAYIRŞubat2022YÖK - 2021-22Aralı

The Amsterdam Declaration on Fungal Nomenclature

The Amsterdam Declaration on Fungal Nomenclature was agreed at an international symposium convened in Amsterdam on 19–20 April 2011 under the auspices of the International Commission on the Taxonomy of Fungi (ICTF). The purpose of the symposium was to address the issue of whether or how the current system of naming pleomorphic fungi should be maintained or changed now that molecular data are routinely available. The issue is urgent as mycologists currently follow different practices, and no consensus was achieved by a Special Committee appointed in 2005 by the International Botanical Congress to advise on the problem. The Declaration recognizes the need for an orderly transitition to a single-name nomenclatural system for all fungi, and to provide mechanisms to protect names that otherwise then become endangered. That is, meaning that priority should be given to the first described name, except where that is a younger name in general use when the first author to select a name of a pleomorphic monophyletic genus is to be followed, and suggests controversial cases are referred to a body, such as the ICTF, which will report to the Committee for Fungi. If appropriate, the ICTF could be mandated to promote the implementation of the Declaration. In addition, but not forming part of the Declaration, are reports of discussions held during the symposium on the governance of the nomenclature of fungi, and the naming of fungi known only from an environmental nucleic acid sequence in particular. Possible amendments to the Draft BioCode (2011) to allow for the needs of mycologists are suggested for further consideration, and a possible example of how a fungus only known from the environment might be described is presented

New records and noteworthy data of plants, algae and fungi in SE Europe and adjacent regions, 15

This paper presents new records and noteworthy data on the following taxa in SE Europe and adjacent regions: saprotrophic fungus Geastrum morganii, Guignardia istriaca and Hypoxylon howeanum, mycorrhizal fungus Amanita friabilis and Suillus americanus, xanthophyte Vaucheria frigida, stonewort Chara hispida, liverwort Calypogeia integristipula and Ricciocarpus natans, moss Campylopus introflexus, Dicranum transsylvanicum, Tortella pseudofragilis and Trematodon ambiguus, fern Ophioglossum vulgatum subsp. vulgatum, monocots Epipactis exilis, Epipactis purpurata and Epipogium aphyllum and dicots Callitriche cophocarpa, Cornus sanguinea subsp. hungarica and Viscum album subsp. austriacum are given within SE Europe and adjacent regions

Deep context of citations using machine‑learning models in scholarly full‑text articles

Information retrieval systems for scholarly literature rely heavily not only on text matching but on semantic- and context-based features. Readers nowadays are deeply interested in how important an article is, its purpose and how influential it is in follow-up research work. Numerous techniques to tap the power of machine learning and artificial intelligence have been developed to enhance retrieval of the most influential scientific literature. In this paper, we compare and improve on four existing state-of-the-art techniques designed to identify influential citations. We consider 450 citations from the Association for Computational Linguistics corpus, classified by experts as either important or unimportant, and further extract 64 features based on the methodology of four state-of-the-art techniques. We apply the Extra-Trees classifier to select 29 best features and apply the Random Forest and Support Vector Machine classifiers to all selected techniques. Using the Random Forest classifier, our supervised model improves on the state-of-the-art method by 11.25%, with 89% Precision-Recall area under the curve. Finally, we present our deep-learning model, the Long Short-Term Memory network, that uses all 64 features to distinguish important and unimportant citations with 92.57% accuracy