Character Features Research Articles

Tight-binding band structure of β- and α-phase Ga2O3 and Al2O3

Rapid design and development of the emergent ultrawide-bandgap semiconductors Ga2O3 and Al2O3 require a compact model of their electronic structures, accurate over the broad energy range accessed in future high-field, high-frequency, and high-temperature electronics and visible and ultraviolet photonics. A minimal tight-binding model is developed to reproduce the first-principles electronic structures of the β- and α-phases of Ga2O3 and Al2O3 throughout their reciprocal spaces. Application of this model to α-Ga2O3/α-Al2O3 superlattices reveals that intersubband transitions can be engineered to the 1.55μm telecommunications wavelength, opening new directions in oxide photonics. Furthermore, by accurately reproducing the bandgap, orbital character, effective mass, and high-energy features of the conduction band, this compact model will assist in the investigation and design of the electrical and optical properties of bulk materials, devices, and quantum confined heterostructures.

An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain.

Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%∼23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper.

The Narrator (Author) and the Hero in T. Shevchenko’s Poem The Funeral Feast •

The study deals with the images of the narrator / author and the prophet – the lyrical character in T. Shevchenko’s poem The Funeral Feast [Tryzna]. The purpose of the study is to comprehend the author’s conception and to reveal the whole range of the ways of expressing the author’s “self ”.In this poem, the lyrical subject is variable. He functions as the author proper as well as the narrating author and the lyrical “I”, sometimes getting the features of the lyrical character. At the same time, the narrator and the character have in common the motif of prophetical service, prophetical mission.The sense structure of the image of the prophet presents in Shevchenko’s poem a synthesis of traditional and original senses. Shevchenko combines the social with the philosophic and the ethical, the universal artistic with the individual ontological. A prophet is not just an artistic image but also a modus of social existence of the writer in his ambition through his works to promote God’s laws of society and personality on the earth as a continuation of life itself.Instead of abstract philosophic speculations and complete negation, the poet creates a special modus of his view of life and attitude towards people. This modus is love; however, it is not love in a simplified, trivial and commonplace meaning but in a deeper, religious-ontological comprehensive sense.The Funeral Feast appears to have the main elements of the motif-symbolic complex of romantic literature: the estrangement of the hero longing for heavenly harmony, prayerful yearning for the heaven he keeps memories of, selectness, loneliness, and orphanhood in earthly captivity, the motifs of the lost heaven, of death as rest and death as meeting. The hero is represented as a creative personality that finds itself in a tragic contradiction with the world.One of the significant semantic oppositions is that of the word and silence. In Shevchenko’s poem, this problem is considered in terms of a transition from silent act to action word. The work on The Funeral Feast actually reveals the insolvability of the contradiction between the prophet’s two guises (“the meek prophet” – “the severe prophet”), thus forming a complex dualistic image.In Shevchenko’s creative development, The Funeral Feast was an important step in comprehending the theme of the poet and his prophetic vocation, it marks a significant stage of the author’s spiritual and creative establishment. The poet in his higher mission is understood in the poem as a personality of a national and supernational, seraphic scale, which determines his role and place in society and in the world. The lyrical-epic nature of the genre made it possible to refer the self-expression of the author’s lyrical “I” to the objectification and personification of the lyrical character. The lyrical subject includes various forms of expressing the author’s consciousness, while the hero conceptualizes the perfect model of a creative personality in his / her prophetic essence.

An RG-FLAT-CRF Model for Named Entity Recognition of Chinese Electronic Clinical Records

The goal of Clinical Named Entity Recognition (CNER) is to identify clinical terms from medical records, which is of great importance for subsequent clinical research. Most of the current Chinese CNER models use a single set of features that do not consider the linguistic characteristics of the Chinese language, e.g., they do not use both word and character features, and they lack morphological information and specialized lexical information on Chinese characters in the medical field. We propose a RoBerta Glyce-Flat Lattice Transformer-CRF (RG-FLAT-CRF) model to address this problem. The model uses a convolutional neural network to discern the morphological information hidden in Chinese characters, and a pre-trained model to obtain vectors with medical features. The different vectors are stitched together to form a multi-feature vector. To use lexical information and avoid the problem of word separation errors, the model uses a lattice structure to add lexical information associated with each word, which can be used to avoid the problem of word separation errors. The RG-FLAT-CRF model scored 95.61%, 85.17%, and 91.2% for F1 on the CCKS 2017, 2019, and 2020 datasets, respectively. We used statistical tests to compare with other models. The results show that most p-values less than 0.05 are statistically significant.

Classical education and Darwinism: Tadeusz Zielinski’s attempt at reconciliation

ABSTRACT Written by a Polish-Russian scholar Tadeusz Zielinski, Our Debt to Antiquity (1903) was a successful attempt to combat the prejudiced view that classical education resists progress. Zielinski argued that Darwinian laws manifest themselves in his discipline in three aspects: (1) in the emergence of Greek and Latin languages as a result of the selection of word-forms and expressions that testify to certain features of national character; (2) in the persistence of classical education across many centuries as the ‘fittest’ educational system; (3) in the selection of schoolchildren for national elites. Although Zielinski’s application of Darwinian laws of selection to human society may appear reactionary to a modern reader, this article argues that in doing so he defends classical education both from the left-wing populists and from attempts at abuse from the right. Moreover, his thoughts prompt modern scholars to reflect on the real presence of Social Darwinism in our present educational system.

A Multitask Deep Learning Framework for DNER.

Over the years, the explosive growth of drug-related text information has resulted in heavy loads of work for manual data processing. However, the domain knowledge hidden is believed to be crucial to biomedical research and applications. In this article, the multi-DTR model that can accurately recognize drug-specific name by joint modeling of DNER and DNEN was proposed. Character features were extracted by CNN out of the input text, and the context-sensitive word vectors were obtained using ELMo. Next, the pretrained biomedical words were embedded into BiLSTM-CRF and the output labels were interacted to update the task parameters until DNER and DNEN would support each other. The proposed method was found with better performance on the DDI2011 and DDI2013 datasets.

The genus Odontothrips in Iran with two new species (Thysanoptera: Thripidae).

Host-plant associations among the 38 species of Odontothrips Amyot Serville, and features of various structural characters in this genus, are discussed. An illustrated key to 9 species occurring in Iran is provided. Two new species are described from Iran based on specimens on flowers of various Fabaceae. O. bezii sp. n. is close to O. mongolicus Pelikan, and O. pitkini sp. n. is considered as the second species in the genus that has a tooth on the distal inner side of antennal segment II.

LINGUISTIC FEATURES OF GLUTTONIC DISCOURSE

The relevance of the work lies in the fact that in modern society, representatives of various nations have their own complexes of taste preferences, manifested in the relevant material, i.e. in gastronomic (glutton) terminology. Each such lexical material makes it possible to highlight the features of the national character.

Contentious Entertainment: The Role of Character and Narrative Features in Shaping Audience Response to Abortion Storylines

In the United States, a growing number of television shows have introduced storylines involving abortion and reproductive health which have the potential to inform and educate viewers. In light of this increase in both the number and diversity of representations, there remain questions regarding their impact on audience attitudes toward this contentious issue. Using a 3 (character disposition) x 2 (consent status) experiment (N = 520), this study examines the influence of a storyline from the television show 13 Reasons Why. By manipulating both the context of the sexual encounter (a narrative feature) and the main character’s affective disposition (a character feature), the study sought to better understand the role such contextual features play in shaping the audience’s response, both directly as well as through their influences on identification. The findings indicate a need for caution in presenting controversial issues on screen: a worrying asymmetry emerged, where negative contextual features promoted less favorable attitudes while positive features had no observable effect. Consequently, abortion depictions could potentially contribute to anti-abortion sentiments if the focal characters are not presented sympathetically and favorably. Thus, enhancing the appeal of these representations should be of paramount importance for conscientious storytellers and practitioners.

Character identification is predicted by narrative transportation, immersive tendencies, and interactivity

The question of what contributes to users’ identification with media characters remains an open issue in research. Apart from media interactivity, user characteristics like gender, age, immersive tendencies, and factual transportation into the narrative are promising factors. Yet, research is still in its infancy, and the usage of different media limits cross-study comparability. Hence, the current study set out to examine predictors of character identification using a text-based fiction with purported interactivity which was inspired by interactive fiction (IF) games. In an online experiment, 228 participants aged 15–65 years were randomly assigned to either an active condition where they could choose from different options, or a passive condition where they only read the story. Additionally, participants filled out questionnaires assessing immersive tendencies, level of identification, and narrative transportation. A multiple linear regression model tested for predictors of character identification. Apart from age and gender which remained non-significant, interactivity, immersive tendencies, and transportation into narrative significantly predicted identification with the IF’s main character. The current findings support theoretical models on media interactivity and identification, yet several open issues such as the role of media content (engaging vs. mundane) and character features (e.g., similarity with user) remain to be answered.

A Character String-Based Stemming for Morphologically Derivative Languages

Morphologically derivative languages form words by fusing stems and suffixes, stems are important to be extracted in order to make cross lingual alignment and knowledge transfer. As there are phonetic harmony and disharmony when linguistic particles are combined, both phonetic and morphological changes need to be analyzed. This paper proposes a multilingual stemming method that learns morpho-phonetic changes automatically based on character based embedding and sequential modeling. Firstly, the character feature embedding at the sentence level is used as input, and the BiLSTM model is used to obtain the forward and reverse context sequence, and the attention mechanism is added to this model for weight learning, and the global feature information is extracted to capture the stem and affix boundaries; finally CRF model is used to learn more information from sequence features to describe context information more effectively. In order to verify the effectiveness of the above model, the model in this paper is compared with the traditional model on two different data sets of three derivative languages: Uyghur, Kazakh and Kirghiz. The experimental results show that the model in this paper has the best stemming effect on multilingual sentence-level datasets, which leads to more effective stemming. In addition, the proposed model outperforms other traditional models, and fully consider the data characteristics, and has certain advantages with less human intervention.

Environmental Migration: a Criminological Aspect

The authors examine the negative social phenomenon of environmental migration from the criminological viewpoint and describe its causes, character and specific features. They present their own classification of the types of environmental migration and offer examples of each type. It is stressed that environmental migration will be growing in the nearest future due to the worsening environmental problems, mainly connected with the violations of the right of citizens to a favorable environment, including the consequences of environmental crimes. There is a link between illegal and environmental migration, which could be disorganized, forced and often uncontrollable, thus posing a threat to national security and leading to a general destabilization of the situation in the country and the world. As a result of their research, the authors conclude that it is required to develop the instruments of international and state policy of regulating environmental migration as a necessary condition for the prevention of the development of illegal migration and forced uncontrolled movement of people and the minimization of the negative consequences of these phenomena. They argue for widening the concept of «refugee» and «displaced person» by including the category «persons who left their place of residence as a result of natural disasters or irreversible natural phenomena caused by climate change». The authors conclude that environmental migration, having a high criminogenic potential, needs further criminogenic research, which will determine its causes, development trends and potential threats. The creation of the mechanism of its regulation and control requires a thoughtful and balanced approach due to the excessive politicizing of environmental problems.

The return of children: a comparative study on the contemporary Turkish and Irish novel

AbstractIn Infancy and History Agamben (1993) suggests, following Benjamin’s footsteps, that true experience is only possible in infancy, a time that has not yet been expropriated by the bareness of modern life. The kind of potentiality that he attributes to infancy signifies the emergence of a new self, which, rather than moving into the mechanical domain of “work,” prefers to remain in the creative territory of “play,” where it is possible to transform old societal structures into new ones.Agamben’s approach to play and its non-chronological temporality offers useful clues in revealing the dynamics of the increasing number of contemporary childhood narratives in Irish and Turkish literature, where characters resist the linear structure of the bildungsroman and the corresponding model of progress, eventually forming a culture of adolescents resistant to maturity. Focusing on some common features of these novelistic characters, such as playfulness, self-experimentation, and messianic idealism, this paper argues that the return of children to contemporary Turkish and Irish novel opens a new terrain of possibilities that offer liberation from the poverty of experience Agamben attributes to modern society.

Deep Neural Networks Algorithm for Vietnamese Word Segmentation

Traditional Vietnamese word segmentation methods do not perform well in the face of Vietnamese ambiguity, in response to the enormous challenge posed by the scarcity of the Vietnamese corpus to language processing. We first investigated the most advanced deep neural network method. According to the ambiguity problem of Vietnamese word segmentation, we then proposed a Vietnamese word segmentation processing technology based on an improved long short-term memory neural network (LSTM), which is made up of an LSTM encoding and a CNN feature extraction portion. The previous important information is kept in the memory unit; the word segmentation processing task is refined into a classification problem and a sequence labeling problem, which can gain the useful features of the word segmentation character and word level automatically. The limitation of the local context window size is avoided, and the word segmentation processing task is refined into a classification problem and a sequence labeling problem. Finally, validated by a homemade Vietnamese news website crawler dataset, the experimental results show that, compared with the single LSTM, single CNN methods, and traditional methods, the performance improvement of our proposed method is more obvious. In the Vietnamese word separation task, the accuracy reaches 96.6%, the recall reaches 95.2%, and the F1 value reaches 96.3%, which is significantly better than the traditional methods CNN and LSTM.

Art’s Work in the Age of Biotechnology: Shaping Our Genetic Futures

Art’s Work in the Age of Biotechnology: Shaping Our Genetic Futures

Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson's Disease.

Parkinson’s disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative movement disorders associated with cognitive dysfunction. The Luria’s Alternating Series Test (LAST) is a clinical tool sensitive to both graphomotor problems and perseverative tendencies that may suggest the dysfunction of prefrontal and/or frontostriatal areas and may be used in PD and PSP assessment. It requires the participant to draw a series of alternating triangles and rectangles. In the study, two clinical groups—51 patients with PD and 22 patients with PSP—were compared to 32 neurologically intact seniors. Participants underwent neuropsychological assessment. The LAST was administered in a paper and pencil version, then scanned and preprocessed. The series was automatically divided into characters, and the shapes were recognized as rectangles or triangles. In the feature extraction step, each rectangle and triangle was regarded both as an image and a two-dimensional signal, separately and as a part of the series. Standard and novel features were extracted and normalized using characters written by the examiner. Out of 71 proposed features, 51 differentiated the groups (p < 0.05). A classifier showed an accuracy of 70.5% for distinguishing three groups.

Palyno-morphological diversity of Asteraceous and Poaceous allergenic plant using microscopic techniques in lesser Himalaya-Pakistan.

In classification and identification of plant species, palyno morphological character has remain helpful for plant taxonomist. The aim to investigation this study was to identify the characteristic features of Asteraceae and Poaceae family member collected from lower Himalayas of Pakistan. Light microscope and Scanning electron microscope were used for feature study of allergenic pollen. Our study includes 10 species of Asteraceae and Poaceae family. The most dominant shape of pollen was Oblate-spheroidal, Prolate-Spheroidal, Prolate and Suboblate shape of pollen. Quantitative character of pollen includes maximum pollen diameter found in Dichanthium annulatum (46.0μm) and minimum polar axis pollen diameter found in Parthenium hysterophorous (16.0μm). Similarly highest value of P/E ratio was noted in Cenchrus echinatus (1.05) and smallest value of P/E ratio was observed in Erigeron bonariensis (0.88). Poaceae family member were monoporate. Character feature of exine sculpturing include Microechinate, granulate, echinate, conical base, lacunae Nanogammate, Aerolate-Scabratre, Verrucate, like variation found in exine of both family member. Highest value of pollen fertility was observed in P. hysterophorous 93%, Similarly maximum value of pollen sterility was noted in E. bonariensis and Brachiaria reptans 24%. This investigation provides the characteristic feature of allergenic pollen species and help to arrange them in define class. RESEARCH HIGHLIGHTS: Pollen has affected many people around the world. Using microscopic techniques, morpho-palynological analysis of Asteraceae and Poaceae families were recognized. LM and SEM study show both qualitative and quantitative character of pollen. Identification of allergenic pollen and microscopic identified character play important role for proper guideline of identification of pollen having allergenic potential.

Temperament, Character Traits and Alexithymia in Patients with Asthma; A University Hospital Sample

Background: The relationship between psychological factors and asthma has received attention since very early times. However, there’s a lack of knowledge about the temperament and character features of asthma patients. We aimed to assess if there are specific personality traits in asthma patients.Methods: Thirty-eight patients with asthma and thirty-five healthy individuals were enrolled in the study. The sociodemographic data form and Temperament and Character Inventory (TCI) and Toronto Alexithymia Scale (TAS) were applied to all participants. Results: The mean age was 40.9 ± 15.9 in the asthma group (52.1%) and 37.3 ± 14.3 in the control group (47.9%). There were 29 females and 9 males in asthma group, 19 females and 16 males in control group. In analysis of temperament and character subscales, the scores of harm avoidance, frugality, sentimentality and transpersonal identification were higher in asthma patients than the control group; the differences were statistically significant (p&amp;lt;0.05). There were no significant differences between the groups for alexithymia (p&amp;gt;0.05).Conclusions: Our study showed that there were character and temperament differences between asthma patients and healthy group. There were no differences for alexithymia between the groups. Further studies are needed to evaluate the causes of differences and the impacts of traits on disease course.

Features of Sacred Music in the Context of the Ukrainian Baroque

The main goal of this article is the research of different genres of spiritual music in the Ukrainian baroque era. This music is decisive for an understanding of Ukrainian culture. In order to achieve this, research following methods was used: comparative-historical, sociocultural, structural, genre-stylistic. Baroque appears as an intermediate between the Renaissance and Age of Enlightenment. Features of the broader character of the Ukrainian civilization explain its cruising between different cultures, correlating between Western culture and Eastern Orthodox culture. The cultural dimension of Ukraine was crossed by different religions: Orthodox, Catholic, Greek-Catholic, and different paths of Protestantism. This fact specified a music of this age. Two basic directions feature specific of spiritual singing of the Ukrainian baroque: partsong (“High baroque”) and spiritual song (“Middle baroque”). Partsong is represented by liturgical and paraliturgical (concerts) genres. This direction was unique because it was a synthesis of Eastern-Christian and Western-Christian tradition (mostly by Catholic musical tradition as multi-chorus composition, musical rhetoric). At the same time, partsong of the orthodox tradition was formed by liturgical tradition. A large influence on the Greek-Catholic church was a catholic music tradition, in which polyphony is not performed “acapella”, but with instrumental accompaniment. Spiritual song was more linked with the catholic tradition and less with the protestant one. It did not have any canonical orthodox genres, but was borrowed by text–music forms formed in Europe in the Age of late Renaissance and early Baroque period. Greek-Catholic tradition was more linked with catholic one. Therefore, this music had a sacred character, becoming a genre of liturgical music. Palimpsest in its confessional dimension became a distinctive feature of the Ukrainian Baroque and created a unique face of the Ukrainian liturgical music.

Offensive-Language Detection on Multi-Semantic Fusion Based on Data Augmentation

The rapid identification of offensive language in social media is of great significance for preventing viral spread and reducing the spread of malicious information, such as cyberbullying and content related to self-harm. In existing research, the public datasets of offensive language are small; the label quality is uneven; and the performance of the pre-trained models is not satisfactory. To overcome these problems, we proposed a multi-semantic fusion model based on data augmentation (MSF). Data augmentation was carried out by back translation so that it reduced the impact of too-small datasets on performance. At the same time, we used a novel fusion mechanism that combines word-level semantic features and n-grams character features. The experimental results on the two datasets showed that the model proposed in this study can effectively extract the semantic information of offensive language and achieve state-of-the-art performance on both datasets.