Description Of Class Research Articles

Conditioning w.r.t. random sets. Part 1: basic notions and results

The most controversial and basic construction in Dempster–Shafer theory is Dempster's combination rule. We analyze this concept through the conditioning with respect to a random set introduced in the paper. The paper has two parts. In Part 1, we describe the basic constructions in Dempster–Shafer theory considering multivalued mappings (random sets), the axiomatic approach proposed by G. Shafer, and the transferable belief model (TBM). We also introduce generalized credal sets as an extension of TBM and the theory of imprecise probabilities. After that, we introduce the conditioning w.r.t. a random set with various kinds of prior information and successive conditioning, and analyze, when this conditioning coincides with Dempster's rule. We show that the successive conditioning can be described by the intersection of generalized credal sets in the case of vacuous prior information and unknown interaction among random sets. Part 2 is devoted to the description of a new class of combination rules constructed by conditioning w.r.t. random sets, and by linear combinations of such rules.

Semistable torsion classes and canonical decompositions in Grothendieck groups

AbstractWe study two classes of torsion classes that generalize functorially finite torsion classes, that is, semistable torsion classes and morphism torsion classes. Semistable torsion classes are parametrized by the elements in the real Grothendieck group up to TF equivalence. We give a close connection between TF equivalence classes and the cones given by canonical decompositions of the spaces of projective presentations due to Derksen–Fei. More strongly, for ‐tame algebras and hereditary algebras, we prove that TF equivalence classes containing lattice points are exactly the cones given by canonical decompositions. One of the key steps in our proof is a general description of semistable torsion classes in terms of morphism torsion classes. We also answer a question by Derksen–Fei negatively by giving examples of algebras that do not satisfy the ray condition. As an application of our results, we give an explicit description of TF equivalence classes of preprojective algebras of type .

Development and validation of a 1D gas–liquid model for dissolved Mn(II) removal by oxidation process in a square bubble column

Modeling multiphase reactors requires an in-depth multidisciplinary analysis of various phenomena including the chemical kinetics, oxygen mass transfer, as well as the simulation of flows within these contactors. In this study, a 1D model of two-phase flow for Mn(II) removal from drinking water by aeration process is developed and validated. This model is based on the Eulerian description of two-phase gas–liquid flow with the comparison between a one-medium bubble size and a two-bubble class description to represent the bubble population in the heterogeneous flow regime. All the relevant chemical species are simulated by coupling hydrodynamics, gas–liquid mass transfer, and reaction kinetic. A pseudo-first-order model accounting for homogeneous and heterogeneous kinetics is considered for Mn(II) oxidation. The performance of the developed 1D model is compared and validated with experimental data. The 1D model was able to predict quantitatively Mn oxidation as a function of pH, initial Mn(II) and initial Mn(IV) concentrations.

A Beginner Book For Arabic Basic User: Analysys Al-Lughah Al-Arabiyah Book Chapter One Based On CEFR Standart

<em>The Arabic textbook MI Grade 1 KSKK is a book published by the Ministry of Religious Affairs in the context of implementing KMA Number 183 of 2019 concerning the PAI Curriculum and Arabic Language in Madrasas. This article presents the results of the analysis of MI Class 1 KSKK Arabic textbooks using CEFR standards. CEFR stands for Common European Framework of Reference established by the Council of Europe in 2001 as a response to the importance of learning foreign languages in order to use them in everyday transactions, be it social, interaction and information transfer. The purpose of this article is to find out the description of the MI Class I Arabic textbook, then at what level is this book in the CEFR standard and how are the CEFR principles contained in it. In its writing, this article uses a literature research method (literature review) with a content analysis approach, where researchers analyze all the material in this MI Class 1 Arabic textbook. The results showed that the Arabic textbooks of MI grade 1 KSKK were categorized in levels A1 and A2 based on CEFR standards. In addition, this book has also fulfilled CEFR principle 6, including language activities, language processes, text, domain, language learning strategies, and language tasks.</em>

Apple scab: resistance to chemical fungicides

The review highlights the situation with the development of resistance to various systemic fungicides in Venturia inaequalis around the world and in Russia, with an assessment of the prospects for their further use. Information is provided on all fungicides used in Russia against apple scab, their effectiveness at a given time, and a description of each chemical class. Data on the mechanisms of resistance to chemicals in the pathogen, including at the molecular level, are presented. The problem of the development of resistance in V. inaequalis and possible ways of its solution at the present level of scientific knowledge are discussed.

Strawberry Volatile Organic Compounds for Targeted Metabolomics: The AMDIS Strawberry User Library from Korean Germplasm

Strawberry aroma, crucial for determining quality, involves complex volatile compounds which are challenging to identify. This study explores strawberry aroma analysis using Gas Chromatography-Mass Spectrometry (GC-MS) and the Automated Mass Spectral Deconvolution and Identification System (AMDIS). Central to our research is the creation of a bespoke strawberry Volatile Organic Compounds (VOCs) user library using AMDIS, specifically for analyzing strawberry aromas. The library contains VOCs from 61 strawberry cultivars, integrating information on 104 VOCs, including mass spectra, retention index, chemical class, CAS number, formula, odor threshold, and odor description. This custom library significantly outperformed a commercial library by reducing potential false hits by 200, decreasing the size of report files by over 96%, and, most importantly, shortening AMDIS analysis processing time from 31 s to 9 s, representing an approximate 71% reduction. Further, the study demonstrates the library’s practical application by contrasting the aroma profiles of strawberries harvested in winter and spring. This comparison revealed significant VOC variations depending on seasonal temperature changes, offering insights into environmental influences on strawberry aroma. In conclusion, this research marks a significant advance in strawberry aroma quality analysis. The strawberry VOC library developed in this study is expected to greatly aid targeted metabolomics and flavor research in strawberry breeding.

Markovian description of a wide class of feedback-controlled systems: application to the feedback flashing ratchet

In feedback-controlled systems, an external agent—the feedback controller—measures the state of the system and modifies its subsequent dynamics depending on the outcome of the measurement. In this paper, we build a Markovian description for the joint stochastic process that comprises both the system and the controller variables. This Markovian description is valid for a wide class of feedback-controlled systems, allowing for the inclusion of errors in the measurement. The general framework is motivated and illustrated with the paradigmatic example of the feedback flashing ratchet.

Perturbative unitarity and the wavefunction of the Universe

Unitarity of time evolution is one of the basic principles constraining physical processes. Its consequences in the perturbative Bunch-Davies wavefunction in cosmology have been formulated in terms of the cosmological optical theorem. In this paper, we re-analyse perturbative unitarity for the Bunch-Davies wavefunction, focusing on: i)i) the role of the i\epsiloniϵ-prescription and its compatibility with the requirement of unitarity; ii)ii) the origin of the different ``cutting rules’’; iii)iii) the emergence of the flat-space optical theorem from the cosmological one. We take the combinatorial point of view of the cosmological polytopes, which provide a first-principle description for a large class of scalar graphs contributing to the wavefunctional. The requirement of the positivity of the geometry together with the preservation of its orientation determine the i\epsiloniϵ-prescription. In kinematic space it translates into giving a small negative imaginary part to all the energies, making the wavefunction coefficients well-defined for any value of their real part along the real axis. Unitarity is instead encoded into a non-convex part of the cosmological polytope, which we name . The cosmological optical theorem emerges as the equivalence between a specific polytope subdivision of the optical polytope and its triangulations, each of which provides different cutting rules. The flat-space optical theorem instead emerges from the non-convexity of the optical polytope. On the more mathematical side, we provide two definitions of this non-convex geometry, none of them based on the idea of the non-convex geometry as a union of convex ones.

A multimodal transfer learning framework for the classification of disaster-related social media images

Rapidly classifying disaster-related social media (SM) images during a catastrophe event is critical for enhancing disaster response efforts. However, the biggest challenge lies in acquiring labeled data for an ongoing (target) disaster to train supervised learning-based models, given that the labeling process is both time-consuming and costly. In this study, we address this challenge by proposing a new multimodal transfer learning framework for the real-time classification of SM images of the target disaster. The proposed framework is based on Contrastive Language-Image Pretraining (CLIP) model, jointly pretrained on a dataset of image-text pairs via contrastive learning. We propose two distinct methods to design our classification framework (1) Zero-Shot CLIP: it learns visual representations from images paired with natural language descriptions of classes. By utilizing the vision and language capabilities of CLIP, we extract meaningful features from unlabeled target disaster images and map them to semantically related textual class descriptions, enabling image classification without training on disaster-specific data. (2) Linear-Probe CLIP: it further enhances the performance and involves training a linear classifier on top of the pretrained CLIP model’s features, specifically tailored to the disaster image classification task. By optimizing the linear-probe classifier, we improve the model’s ability to discriminate between different classes and achieve higher performance without the need for labeled data of the target disaster. Both methods are evaluated on a benchmark X (formerly Twitter) dataset comprising images of seven real-world disaster events. The experimental outcomes showcase the efficacy of the proposed methods, with Linear-Probe CLIP achieving a remarkable 7% improvement in average F1-score relative to the state-of-the-art methods.

Использование машинного обучения для тематической разметки текстовых материалов корпуса устной речи

The research aims to determine the effectiveness of the thesaurus method for forming a list of topic classes when using machine learning for the topic classification of text materials of sociolinguistic interviews. The paper considers the potential of using machine learning in the topic annotation of linguistic corpus materials. The polytopical nature of the analyzed material is due to its genre belonging to dialogical speech. The hierarchical structure of the topics, identified as a result of a preliminary introspective analysis of the texts, can be described using a thesaurus. The results of using the unsupervised machine learning method are discussed involving two sets of topic class names: a list of topics used in manual text annotation and an extended list of micro-topics whose names were selected from a Russian language thesaurus. The paper is novel in that it is the first to propose the thesaurus method for selecting topic labels for the zero-shot classification of weakly structured Russian texts. The research findings show that using a more detailed lexical description for topic classes improves the classification result.

Contrastive Prototype-Guided Generation for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen classes, while only samples from seen classes are available for training. The mainstream methods mitigate the lack of unseen training data by simulating the visual unseen samples. However, the sample generator is actually learned with just seen-class samples, and semantic descriptions of unseen classes are just provided to the pre-trained sample generator for unseen data generation, therefore, the generator would have bias towards seen categories, and the unseen generation quality, including both precision and diversity, is still the main learning challenge. To this end, we propose a Prototype-Guided Generation for Generalized Zero-Shot Learning (PGZSL), in order to guide the sample generation with unseen knowledge. First, unseen data generation is guided and rectified in PGZSL by contrastive prototypical anchors with both class semantic consistency and feature discriminability. Second, PGZSL introduces Certainty-Driven Mixup for generator to enrich the diversity of generated unseen samples, while suppress the generation of uncertain boundary samples as well. Empirical results over five benchmark datasets show that PGZSL significantly outperforms the SOTA methods in both ZSL and GZSL tasks.

Формирование монологической речи студентов неязыковых вузов Казахстана при обучении русскому языку как иностранному

When teaching a foreign language, special attention is paid to the ability to conduct a dialogue in different language situations, to maintain a conversation on various topics. However, no less important is the formation of students' monologue speech skills, a necessary indicator of an accomplished professional. At the same time, methodological methods and techniques for working with this skill have not yet been widely covered. In this regard, the authors of the article raise the actual problem of the formation of monologue speech skills in groups with different levels of Russian language proficiency. The purpose of the article is to present and determine the effectiveness of innovative methods for practicing skills of both pre-prepared and spontaneous monologue speech in groups with different language levels. The authors consider such methods as «Tandem», lesson-excursion, case study, project. The features of the methods are revealed that allow them to be effectively applied in practicing the skills of monologue speech; a description and analysis of specific classes using these methods in multi-level groups at a technical university are presented. The main attention is paid to the description of the specific stages of organizing classes according to the proposed methods. Comparative analysis of the results of groups trained with and without these techniques allows us to clearly demonstrate the effectiveness of the proposed forms of work.

Finding Interpretable Class-Specific Patterns through Efficient Neural Search

Discovering patterns in data that best describe the differences between classes allows to hypothesize and reason about class-specific mechanisms. In molecular biology, for example, these bear the promise of advancing the understanding of cellular processes differing between tissues or diseases, which could lead to novel treatments. To be useful in practice, methods that tackle the problem of finding such differential patterns have to be readily interpretable by domain experts, and scalable to the extremely high-dimensional data. In this work, we propose a novel, inherently interpretable binary neural network architecture Diffnaps that extracts differential patterns from data. Diffnaps is scalable to hundreds of thousands of features and robust to noise, thus overcoming the limitations of current state-of-the-art methods in large-scale applications such as in biology. We show on synthetic and real world data, including three biological applications, that unlike its competitors, Diffnaps consistently yields accurate, succinct, and interpretable class descriptions.

ASPHALT- RESIN- PARAFFIN ASSOCIATION CRUSHING IN THE VOLUME OF OIL AND OIL EMULSION WITH NEW NANOSTRUCTURE COORDINATION POLYMERS

A short list of existing methods for the prevention and removal of asphalt-resin-paraffin deposits from oilfield equipment is given. Chemical methods related to the use of various additives, reagents, and remover agents are discussed in more detail. A brief description of the main classes of chemicals used in solving the problem of preventing and removing asphalt-resin-paraffin deposits is given. It has been shown that in order to select the most chemically effective ways to prevent and remove organic deposits, it is necessary to obtain an adequate understanding of the composition, properties, and structure of the source oil and the formed deposits. The mechanism of action of the reagents on asphalt-resin-paraffin deposits was clarified, and it was established that the reagents BAF-1 and BAF-2 (technical conditional name of composites) when interacting with the deposits, grind the associates and ensure their uniform distribution in the oil, which improves the rheological properties of the oil. It is indicated that the technology being put forward is based on the clathrate formation, i.e. Lewis's theory of "host-guest". Keywords: asphalt-resin-paraffin deposits, crushing, factors affecting the formation of ARPD, coagulation, the chemical composition of oil, mechanism of ARPD formation, methods of ARPD control.

Improving the Recognition Performance of Apnea from Electrocardiogram Signal Based on Convolutional Neural Network (CNN)

Due to the consequences of apnea heart disease, include high blood pressure, heart failure, and heart attacks, early diagnosis is important and has now become a subject of interest for researchers in the world. So far, many researches have been done in this field. Among these, we can mention the use of various pre-processing in the field of time and frequency, along with classifications based on machine learning. These classifications work well in most cases. But in cases where the number of features extracted from the input data increases or the hidden patterns in the data are complex, they do not perform well. In addition to classification, the poor performance of these studies can be caused by pre-processing or feature extraction itself. In this article, in order to solve the problems of previous studies, pre-processing of the ECG signal has been done using a Butterworth pass filter, Golay Savizki filter and wavelet transform. Pre-processing makes the R peaks in the input to be well distinguished. Finally, by extracting the exact position of the R-R interval in the ECG with minimal error, efficient features that enable the proper description of different classes can be extracted. These features include the wavelet, can provide good results due to their time-frequency nature. The simulation results also show these features well. In terms of choosing the classification, a convolutional neural network (CNN) has been used to diagnose the disease. The accuracy percentage of 97.91% obtained from the simulation of the proposed method on the sleep apnea database of University College Dublin is a proof of good performance of this method.

Uncertainty Feature Learning With Personalized Class Description for Visual Domain Adaptation

Uncertainty Feature Learning With Personalized Class Description for Visual Domain Adaptation

Representations of Smith algebras which are free over the Cartan subalgebra

In this paper, we study the category of modules over the Smith algebra which are free of finite rank over the unital polynomial subalgebra generated by the Cartan element h and obtain families of such simple modules of arbitrary rank. In the case of rank one we obtain a full description of the isomorphism classes, a simplicity criterion, and an algorithm to produce all composition series. We show that all such modules have finite length and describe the composition factors and their multiplicity.

Inferences of Masculinity and Femininity Across Intersections of Social Class and Gender: A Social Structural Perspective.

This research employs a social structural perspective to analyze the content of intersectional social class and gender stereotypes. We investigated how the structural positioning of class and gender categories differentially foster inferences of masculinity and femininity. The social structures that organize class and gender differ: Class is marked by access to resources, and gender is marked by a division of labor for care work. Thus, we examined whether masculinity inferences more strongly varied by social class and whether femininity inferences more strongly varied by gender categories. In Study 1, a total 427 undergraduates provided open-ended descriptions of social class and gender groups. In Study 2, a total 758 undergraduates rated the same groups on preselected trait measures. In Study 3, a total 83 adult participants considered a vignette that manipulated a target's structural resources and gender. Across datasets, variation in social class primarily influenced inferences about masculinity while variation in gender primarily influenced inferences about femininity.

Psychosocial work characteristics and sleep quality among early career registered nurses: a cross-sectional latent profile analysis

BackgroundIndividual psychosocial work characteristics have been associated with health and well-being of registered nurses. However, it is yet to be determined whether different types of psychosocial work characteristics form patterned profiles and how these profiles are associated with the health and well-being. The purpose of this study was to identify latent psychosocial work characteristic profiles, including procedural, interactional and distributive justice, job demand and job control, and examine whether the profiles are associated with sleep quality among early career registered nurses.MethodsWe conducted a cross-sectional study comprising 632 early career registered nurses. Data were collected between November and December 2018 using an electronic survey with internationally validated measures including the Organizational Justice Scale, the Nurse Stress Index Scale, the Job Content Questionnaire, and the Sleep Problems Questionnaire. Latent profile analysis was used to identify groups with similar psychosocial work characteristic profiles. Multinomial and linear regression analyses were used to examine the association between latent work characteristics profiles and sleep quality.ResultsAnalysis yielded four profiles. The profiles were named based on the descriptions of classes as high strain/low justice, medium strain/high justice, medium strain/medium justice, and low strain/high justice. The low strain/high justice profile group (p = < 0.001) and the medium strain/high justice profile group (p = 0.002) had statistically significantly better sleep quality compared to the high strain/low justice profile group.ConclusionsHigh procedural and interactional justice may alleviate strain in early career registered nurses and protect them against sleep problems. Promoting organizational justice in early career stages seems an efficient way to enhance registered nurses’ well-being and sleep quality.

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.

The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.