Littérature scientifique sur le sujet « Wide subcategories »

We explore some properties of wide subcategories of the category [Formula: see text] of finitely generated left [Formula: see text]-modules, for some artin algebra [Formula: see text] In particular we look at wide finitely generated subcategories and give a connection with the class of standard modules and standardly stratified algebras. Furthermore, for a wide class [Formula: see text] in [Formula: see text] we give necessary and sufficient conditions to see that [Formula: see text] for some projective [Formula: see text]-module [Formula: see text] and finally, a connection with ring epimorphisms is given.

As a professional value, the love of the profession can significantly affect nurses’ professional practice, behaviors and commitment. Many different factors can affect the love of the profession. The exploration of nurses’ experiences of these factors can provide valuable data for development of the love of the profession. The aim of this study was to explore nurses’ perceptions of the factors contributing to the development of the love of the profession. This qualitative study was conducted in 2020–2021 using the conventional content analysis approach. The participants were thirteen nurses with different organizational positions purposively recruited from different settings in Iran. The data were collected via semi-structured interviews, and were analyzed via the conventional content analysis approach proposed by Graneheim and Lundman. The factors contributing to the development of the love of the profession were categorized into four main categories, namely the public perception of the profession (with three subcategories), educational variables (with two subcategories), the characteristics of the profession (with four subcategories), and nurses’ self-evaluation (with three subcategories). The love of the profession is affected by a wide range of personal, educational, professional and social factors. The manipulation of these factors would help to develop nurses’ and nursing students’ love of the profession, and encourage people to choose nursing as a career.

AbstractThe purpose of this paper is to understand lattices of certain subcategories in module categories of representation-finite gentle algebras called tiling algebras, as introduced by Coelho Simões and Parsons. We present combinatorial models for torsion pairs and wide subcategories in the module category of tiling algebras. Our models use the oriented flip graphs and noncrossing tree partitions, previously introduced by the authors, and a description of the extension spaces between indecomposable modules over tiling algebras. In addition, we classify two-term simple-minded collections in bounded derived categories of tiling algebras. As a consequence, we obtain a characterization of c-matrices for any quiver mutation-equivalent to a type A Dynkin quiver.

Abstract Background: Polyphenols are natural compounds synthesized exclusively by plants with chemical features related to phenolic substances and eliciting strong antioxidants properties. Objective: The aim of this paper is to give a reliable overview of the chemical classification of natural polyphenols. Methods: Literature survey was done through google scholar, pubmed and scopus search engine. Results and Discussion: These molecules or classes of natural substances are characterized by two phenyl rings at least and one or more hydroxyl substituents. This description comprehends a large number of heterogeneous compounds with reference to their complexity. Therefore, polyphenols can be simply classified into flavonoids and non-flavonoids, or be subdivided in many sub-classes depending on the number of phenol units within their molecular structure, substituent groups, and/or the linkage type between phenol units. Polyphenols are widely distributed in plant tissues where they mainly exist in form of glycosides or aglycones. The structural diversity of flavonoid molecules arises from variations in hydroxylation pattern and oxidation state resulting in a wide range of compounds: flavanols, anthocyanidins, anthocyanins, isoflavones, flavones, flavonols, flavanones, and flavanonols.

Torsion pairs were introduced by Dickson in 1966 as a generalization of the concept of torsion abelian group to arbitrary abelian categories. Using torsion pairs, we can divide complex abelian categories in smaller parts which are easier to understand. In this thesis we discuss torsion pairs in the category of modules over a finite-dimensional algebra, in particular we explore the relation between torsion pairs in the category of all modules and torsion pairs in the category of finite-dimensional modules. In the second chapter of the thesis, we present the analogue of a classical theorem of Auslander in the context of τ-tilting theory: for a finite-dimensional algebra the number of torsion pairs in the category of finite-dimensional modules is finite if and only if every brick over such algebra is finite- dimensional. In the third chapter, we revisit the Ingalls-Thomas correspondences between torsion pairs and wide subcategories in the context of large torsion pairs. We provide a nice description of the resulting wide subcategories and show that all such subcategories are coreflective. In the final chapter, we describe mutation of cosilting modules in terms of an operation on the Ziegler spectrum of the algebra.

The System Wide Risk Assessment (SWRA) is an essential first step in the pipeline integrity management program. It is required by both Canadian and US regulators and is expected to estimate risk due to all threats, interaction of threats, and consequences. The main objective of the SWRA is to identify high risk segments so that segments with excessive risk can be mitigated. The SWRA models developed in this study employs quantified likelihood models and consequence models. A companion paper explains the consequence models. This paper presents the framework and rationale used to produce quantifiable measures of likelihood for each threat. The quantification enables sensible comparisons between threat likelihood values and also enables realistic combining of likelihood values to produce total likelihood of failure due to all threats. It also facilitates identification of key parameters that contribute to each threat. It is important to have a consistent risk framework that systematically applies to all the threats and accommodates all the different aspects and mitigative actions in each threat management process. For effective continuous improvement it is essential that the models are transparent and updatable. A consistent framework that is systematic, rigorous, transparent and updatable is utilized with explicit consideration to threat interactions. The main advantages of the likelihood models developed in this study are: • It is based on all evidence that is available for each threat (failure histories, observations from assessments, i.e., digs, HTs, and ILIs, and mechanistic understanding) • It considers all nine threat categories and relevant subcategories where causal factors are different (such as SCC and Circumferential SCC within the crack threat category) • It clearly considers all three types of threat interactions (Interacting coincident defects, Interacting-activating threats, and Interacting common-mode conditions) among all threat categories. • It is based on subsystem specific historical failure rates for each threat, where subsystem is defined as a subset of pipelines that have different performance characteristics with respect to at least one threat. This basis enables the failure frequencies predicted to be more in line with reality and consequently improves accuracy of predictions and appropriate quantification. • The subsystem specific historical failure rates are then calibrated to correlate to different mechanistic characteristics so that within-pipeline-subsystem variation due to changes in parameters is represented. • Finally assessments or observations are used to appropriately update threat likelihood with latest knowledge from measured local observations. All of the improvements mentioned above have helped the SWRA 2013 to produce more representative results. The comprehensive set of validation exercises verify that the results are realistic.

Abstract Well cementing has evolved tremendously since its first application in the early 1900s. In the past, cement was mixed with water at the optimal ratio and combined with silica, bentonite, and additives according to the conditions of use. This simple formulation cannot serve the full breadth of oilfield applications. As a result, cement blend composition has evolved with advanced materials such as lightweight glass beads, cenospheres, polymeric beads, hematite, silica, manganese tetroxide, and many more. The wide variety of material used combined with poor understanding of the modern blend has resulted in operational issues, causing failures in blend delivery and execution. There have been cases of unfavorable blend leading to operation failure after it got stuck within the silo, unable to be pneumatically transferred. Some blend has high segregation potential, causing components to separate out, leading to problems in terms of mixing and having stable density during execution. The focus of this study is to establish a comprehensive understanding of modern cement blend additives for seamless operational execution. Several commonly used materials have been selected to form a case study of powder additive behavior. These materials are grouped into three categories: light, medium, and heavy density, with specific gravity between 0.1 and 1.9, 2.0 and 3.9, and 4.0 and 6.0 g/cm3, respectively. Each group is further divided into subcategories based on the particle sizes of fine, medium, and coarse. These materials are then characterized in terms of flowability factor, aeration energy, and compaction ratio, which consists of the Carr index and Hausner ratio. These are typical physical flow characteristics of the bulk solids. Results show that particle size and density significantly influence the flowability factor, aeration energy, and compaction ratio of a powder blend. In general, materials with fine particle size tend to have higher resistance to flow when evaluated through the flowability factor. Both medium- and coarse-particle additives tend to have higher flowability factor than fine-particle blends, that results in easier blend movement. Aeration energy requirements are much higher for high-density and coarse particles compared to medium and fine particles. The compaction ratio evaluation shows that coarse materials have lower tendency to compact compared to the fine and medium materials. Based on the established understanding of individual components, mixtures are then formed with the intention of improving the overall blend character. The poor characteristics of a high-density fine material are significantly improved by combining the fine material with a lightweight cenosphere. The high aeration energy requirements of heavy coarse particles can be halved by adding lightweight glass beads. For improved behavior, a different particle size of silica materials can be mixed at optimized ratio. Combining materials to obtain optimal particle-size distribution and density is crucial to ensuring an overall blend with favorable characteristics. The behavior of individual components based on particle size and density has paved the way for effective optimization of blends for seamless operational deliverables