Set Of Categories Research Articles

Revisiting the modes of China's revisionism: a comment on Natalizia and Termine

AbstractGabriele Natalizia and Lorenzo Termine lay out an innovative framework to analyze the trajectory of the current foreign policy of the People's Republic of China. As it stands, it suffers from serious conceptual ambiguities and generates a set of categories that is too large to guide empirical research. After revision, however, the framework that Natalizia and Termine propose can be deployed to elucidate Beijing's behavior in various parts of the world, most notably the Persian Gulf.

Signals of sustainability transition: Sensing enabling factors through cultural initiatives

This article reports a first attempt to combine the analysis of socio-technical mechanisms and initiative-based learning to sense sustainability transition signals in cities. Relying on the analysis of cultural initiatives in six European Cities, the study identifies those factors that enabled social impact generation. It then formulates hypotheses about their contribution to the inception and rooting of sustainability transition dynamics. As a result, the article proposes a set of analytical categories of enabling factors acting across niches and regimes. The same factors are then reinterpreted by referring to scaling mechanisms (scaling up, out and deep). The proposed analytical scheme seeks to offer a broader reflection on the conceptual and methodological challenges related to sensing and interpreting urban sustainability transition pathways.

Contextual Specificity of (Un)Healthy Food/Drink Intake in Everyday Life: A Study Based on Episodic Memories

Abstract Identifying the contexts of episodes of (un)healthy food/drink intake could inform strategies for eating more healthily. This study assessed memories of recent episodes of healthy and unhealthy eating from adults in Mexico. For each (un)healthy eating episode participants recalled place, time of day, people present and food/drink intake. Categories were formed for the contextual features and foods/drinks that were reported, then the relative frequency of each category was tested between healthy and unhealthy eating episodes. Overall, there was a large set of categories of (un)healthy food/drink choices, and there were more healthy eating episodes with family at home and unhealthy eating episodes with friends out of home. However, as expected, a more specific context as well as food/drink intake was identified for each sort of recalled (un)healthy eating episode of the day. Additionally, eating out of home, later in the day and with people present were features related to higher estimated energy content across (un)healthy eating episodes. These findings support the assessment of self-reported memories of recent eating episodes to generate evidence that contribute to contexts that support healthy eating habits.

The effect of the scale of grant scoring on ranking accuracy

In this study we quantify the accuracy of scoring the quality of research grants using a finite set of distinct categories (1, 2, …., k), when the unobserved grant score is a continuous random variable comprising a true quality score and measurement error, both normally distributed. We vary the number of categories, the number of assessors that score the same grant and a signal-to-noise ratio parameter. We show that the loss of information of scoring a small number of categories (k > 5) compared to scoring on a continuous scale is very small, so that increasing the number of scoring categories is unlikely to lead to an improvement in the outcomes of scoring systems. In addition, we model the effect of grant assessors scoring too close to the mean and show that this results in only a very small reduction in the accuracy of scoring.

Towards classification of images by using block-based CNN

Image classification is the process of assigning labeling to the input images to a fixed set of categories; however, assigning labels to the image is difficult by using the traditional method because of the large number of images. To solve this problem, we will resort to deep learning techniques. Which is enables computers to recognize and extract visual characteristics. The convolutional neural network (CNN) is a deep neural network used for many purposes, such as image classification, detection, and face recognition, due to its high-performance accuracy in classification and detection tasks. In this paper, we develop CNN based on the transfer learning approach for image classification. The network comprises two types of transfer learning, ResNet and DenseNet, as building blocks of the network with an multilayer perceptron (MLP) classifier. The proposed method does not need to preprocess before these datasets that input into the network. It was train on two datasets: the Cifar-10 and the Sign-Traffic datasets. We conclude that the proposed method achieves the best performance compared with other states of the art. The accuracy gained is 97.45% and 99.45%, respectively, where the proposed CNN increased the accuracy compared to other methods by 3%.

Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models

Across many industries, visual quality assurance has transitioned from a manual, labor-intensive, and error-prone task to a fully automated and precise assessment of industrial quality. This transition has been made possible due to advances in machine learning in general, and supervised learning in particular. However, the majority of supervised learning approaches only allow to identify pre-defined categories, such as certain error types on manufactured objects. New, unseen error types are unlikely to be detected by supervised models. As a remedy, this work studies unsupervised models based on deep neural networks which are not limited to a fixed set of categories but can generally assess the overall quality of objects. More specifically, we use a quality inspection case from a European car manufacturer and assess the detection performance of three unsupervised models (i.e., Skip-GANomaly, PaDiM, PatchCore). Based on an in-depth evaluation study, we demonstrate that reliable results can be achieved with fully unsupervised approaches that are even competitive with those of a supervised counterpart.

On Semi-Continuous and Clisquish Functions in Generalized Topological Spaces

In this paper, we will focus on three types of functions in a generalized topological space, namely; lower and upper semi-continuous functions, and cliquish functions. We give some results for nowhere dense sets and for second category sets. Further, we discuss the nature of cliquish functions in generalized metric spaces and provide the characterization theorem for cliquish functions in terms of nowhere dense sets.

Genomic vulnerability to climate change in Quercus acutissima, a dominant tree species in East Asian deciduous forests.

Understanding the evolutionary processes that shape the landscape of genetic variation and influence the response of species to future climate change is critical for biodiversity conservation. Here, we sampled 27 populations across the distribution range of a dominant forest tree, Quercus acutissima, in East Asia, and applied genome-wide analyses to track the evolutionary history and predict the fate of populations under future climate. We found two genetic groups (East and West) in Q.acutissima that diverged during Pliocene. We also found a heterogeneous landscape of genomic variation in this species, which may have been shaped by population demography and linked selections. Using genotype-environment association analyses, we identified climate-associated SNPs in a diverse set of genes and functional categories, indicating a model of polygenic adaptation in Q.acutissima. We further estimated three genetic offset metrics to quantify genomic vulnerability of this species to climate change due to the complex interplay between local adaptation and migration. We found that marginal populations are under higher risk of local extinction because of future climate change, and may not be able to track suitable habitats to maintain the gene-environment relationships observed under the current climate. We also detected higher reverse genetic offsets in northern China, indicating that genetic variation currently present in the whole range of Q.acutissima may not adapt to future climate conditions in this area. Overall, this study illustrates how evolutionary processes have shaped the landscape of genomic variation, and provides a comprehensive genome-wide view of climate maladaptation in Q.acutissima.

Label informed hierarchical transformers for sequential sentence classification in scientific abstracts

AbstractSegmenting scientific abstracts into discourse categories like background, objective, method, result, and conclusion is useful in many downstream tasks like search, recommendation and summarization. This task of classifying each sentence in the abstract into one of a given set of discourse categories is called sequential sentence classification. Existing machine learning‐based approaches to this problem consider the content of only the abstract to obtain the neural representation of each sentence, which is then labelled with a discourse category. But this ignores the semantic information offered by the discourse labels themselves. In this paper, we propose LIHT, Label Informed Hierarchical Transformers – a method for sequential sentence classification that explicitly and hierarchically exploits the semantic information in the labels to learn label‐aware neural sentence representations. The hierarchical model helps to capture not only the fine‐grained interactions between the discourse labels and the words in the abstract at the sentence level but also the potential dependencies that may exist in the label sequence. Thus, LIHT generates label‐aware contextual sentence representations that are then labelled with a conditional random field. We evaluate LIHT on three publicly available datasets, namely, PUBMED‐RCT, NICTA‐PIBOSO and CSAbstract. The incremental gain in F1‐score in all the three cases over the respective state‐of‐the‐art approaches is around . Though the gains are modest, LIHT establishes a new performance benchmark for this task and is a novel technique of independent interest. We also perform an ablation study to identify the contribution of each component of LIHT in the observed performance, and a case study to visualize the roles of the different components of our model.

Dialectica principles via Gödel doctrines

Gödel's Dialectica interpretation was conceived as a tool to obtain the consistency of Heyting arithmetic in the 40s. In recent years, several proof theoretic transformations, based on Gödel's Dialectica interpretation, have been used systematically to extract new content from classical proofs, following a suggestion of Kreisel. Thus, the interpretation has found new relevant applications in several areas of mathematics and computer science. Several authors have explained the Dialectica interpretation in categorical terms. In particular, de Paiva introduced the notion of a Dialectica category as an internal version of Gödel's Dialectica Interpretation in her doctoral work. This was generalised by Hyland and Hofstra, who considered the interpretation in terms of fibrations. In our previous work, we introduced an intrinsic presentation of the Dialectica construction via a generalisation of Hofstra's work, using the notion of Gödel fibration and its proof-irrelevant version, a Gödel doctrine. The key idea is that Gödel fibrations (and doctrines) can be thought of as fibrations generated by some basic predicates playing the role of quantifier-free predicates. This categorification of quantifier-free predicates is crucial not only to show that our notion of Gödel fibration is equivalent to Hofstra's Dialectica fibration in the appropriate way, but also to show how Gödel doctrines embody the main logical features of the Dialectica Interpretation. To show that, we derive the soundness of the interpretation of the implication connective, as expounded by Troelstra, in the categorical model. This requires extra logical principles, going beyond intuitionistic logic, namely (suitable versions of) the Markov Principle (MP) and the Independence of Premise (IP) principle, as well as some choice. We show how these principles are satisfied in the categorical setting, establishing a tight correspondence between the logical system and the categorical framework. This tight correspondence should come in handy not only when discussing the traditional applications of Dialectica, but also when dealing with some newer uses of the interpretation, as in modelling games or concurrency theory. Finally, to complete our analysis, we characterise categories obtained as a result of the Hyland, Johnstone and Pitts tripos-to-topos construction when applied to Gödel doctrines.

Social Movement Sustainability on Social Media: An Analysis of the Women’s March Movement on Twitter

Social media has emerged as a powerful medium to organize and mobilize social movements. In particular, the connective action of social media builds associations and allows for the continuity of social movements. Yet there is a lack of research on how connective action emergent from social media messages sustains long-term social movements. Accordingly, in this study, we concentrate on Twitter messages related to Women’s March protests held in 2017, 2018, and 2019. Using an interpretive analysis followed by the topic modeling approach, we analyzed the tweets to identify the different types of messages associated with the movement. These messages were classified using a set of categories and subcategories. Furthermore, we conducted a temporal analysis of the message (sub)categories to understand how distinct messages allow movement continuity beyond a specific protest march. Results suggest that while most of the messages are used to motivate and mobilize individuals, the connective action tactics employed through messages sent before, during, and after the marches allowed Women’s March to become a broader and more persistent movement. We advance theoretical propositions to explain the sustainability of a long-term social movement on social media, exemplified through large-scale connective action that persists over time. In doing so, this study contributes to connective action research by providing message categorization that synthesizes the meaning of message content. The findings could help social movement organizers learn different ways to frame messages that resonate with broader social media users. Moreover, our approach to analyzing a large set of tweets might interest other qualitative researchers.

The meaning distributions on different levels of granularity

The meaning distributions of certain linguistic forms generally follow a Zipfian distribution. However, since the meanings can be observed and classified on different levels of granularity, it is thus interesting to ask whether their distributions on different levels can be fitted by the same model and whether the parameters are the same. In this study, we investigate three quasi-prepositions in Shanghainese, a dialect of Wu Chinese, and test whether the meaning distributions on two levels of granularity can be fitted by the same model and whether the parameters are close. The results first show that the three models proposed by modern quantitative linguists can both achieve a good fit for all cases, while both the exponential (EXP) model and the right-truncated negative binomial (RTBN) models behave better than the modified right-truncated Zipf-Alekseev distribution (MRTZA), in terms of the consistency of the goodness of fit, parameter change, rationality, and simplicity. Second, the parameters of the distributions on the two levels and the curves are not exactly the same or even close to each other. This has supported a weak view of the concept of ‘scaling’ in complex sciences. Finally, differences are found to lie between the distributions on the two levels. The fine-grained meaning distributions are more right-skewed and more non-linear. This is attributed to the openness of the categories of systems. The finer semantic differentiation behaves like systems with open set of categories, while the coarse-grained meaning distribution resembles those having a close set of few categories.

Similarity function for one-shot learning to enhance the flexibility of myoelectric interfaces.

This study aims to develop a flexible myoelectric pattern recognition (MPR) method based on one-shot learning, which enables convenient switching across different usage scenarios, thereby reducing the re-training burden. First, a one-shot learning model based on a Siamese neural network was constructed to assess the similarity for any given sample pair. In a new scenario involving a new set of gestural categories and/or a new user, just one sample of each category was required to constitute a support set. This enabled the quick deployment of the classifier suitable for the new scenario, which decided for any unknown query sample by selecting the category whose sample in the support set was quantified to be the most like the query sample. The effectiveness of the proposed method was evaluated by experiments conducting MPR across diverse scenarios. The proposed method achieved high recognition accuracy of over 89% under the cross-scenario conditions, and it significantly outperformed other common one-shot learning methods and conventional MPR methods (p < 0.01). This study demonstrates the feasibility of applying one-shot learning to rapidly deploy myoelectric pattern classifiers in response to scenario change. It provides a valuable way of improving the flexibility of myoelectric interfaces toward intelligent gestural control with extensive applications in medical, industrial, and consumer electronics.

Toward representative genomic research: the children's rare disease cohorts experience.

Due to racial, cultural, and linguistic marginalization, some populations experience disproportionate barriers to genetic testing in both clinical and research settings. It is difficult to track such disparities due to non-inclusive self-reported race and ethnicity categories within the electronic health record (EHR). Inclusion and access for all populations is critical to achieve health equity and to capture the full spectrum of rare genetic disease. We aimed to create revised race and ethnicity categories. Additionally, we identified racial and ethnic under-representation amongst three cohorts: (1) the general Boston Children's Hospital patient population (general BCH), (2) the BCH patient population that underwent clinical genomic testing (clinical sequencing), and (3) Children's Rare Disease Cohort (CRDC) research initiative participants. Race and ethnicity data were collected from the EHRs of the general BCH, clinical sequencing, and CRDC cohorts. We constructed a single comprehensive set of race and ethnicity categories. EHR-based race and ethnicity variables were mapped within each cohort to the revised categories. Then, the numbers of patients within each revised race and ethnicity category were compared across cohorts. There was a significantly lower percentage of Black or African American/African, non-Hispanic/non-Latine individuals in the CRDC cohort compared with the general BCH cohort, but there was no statistically significant difference between the CRDC and the clinical sequencing cohorts. There was a significantly lower percentage of multi-racial, Hispanic/Latine individuals in the CRDC cohort than the clinical sequencing cohort. White, non-Hispanic/non-Latine individuals were over-represented in the CRDC compared to the two other groups. We highlight underrepresentation of certain racial and ethnic populations in sequencing cohorts compared to the general hospital population. We propose a range of measures to address these disparities, to strive for equitable future precision medicine-based clinical care and for the benefit of the whole rare disease community.

How do organisations communicate aspects related to their social performance? A proposed set of indicators and metrics for sustainability reporting

The aim of this study is to determine whether it is possible to calculate indicators capable of measuring the social performance of organisations from the information that they are currently communicating in their sustainability reports (SR). To this end, a three-step methodology is proposed and applied to a case study. As a starting point, 11 frameworks to measure the social performance at the organisational level have been reviewed and the indicators of each of them were identified, classified and grouped into a common set of categories. Then, the social information communicated in a representative sample of SR was identified. As a result, 42 indicators grouped into 9 categories have been proposed. For each of them, a set of metrics and units were also proposed, based on the information that organisations commonly use in their SR. Finally, the set of indicators were applied to a Spanish organisation belonging to the energy utilities sector. This study helps to assess and improve the social performance of organisations by providing them with a clear guide on how to measure and track it using data that they already communicate in their SR.

Cross-Domain Contrastive Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. Most existing UDA methods learn domain-invariant feature representations by minimizing feature distances across domains. In this work, we build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets. Exploring the same set of categories shared by both domains, we introduce a simple yet effective framework CDCL, for domain alignment. In particular, given an anchor image from one domain, we minimize its distances to cross-domain samples from the same class relative to those from different categories. Since target labels are unavailable, we use a clustering-based approach with carefully initialized centers to produce pseudo labels. In addition, we demonstrate that CDCL is a general framework and can be adapted to the data-free setting, where the source data are unavailable during training, with minimal modification. We conduct experiments on two widely used domain adaptation benchmarks, i.e., Office-31 and VisDA-2017, for image classification tasks, and demonstrate that CDCL achieves state-of-the-art performance on both datasets.

TELAAH STRUKTURALISME GENETIK LUCIEN GOLDMANN DALAM PENGKAJIAN KARYA SASTRA PUISI GADIS PEMINTA-MINTA KARYA TOTO SUDARTO

This article attempts to discuss the study of genetic structuralism by Lucien Goldman in his study of the begging girl poem by Toto Sudarto. In the theory of genetic structuralism, Goldman builds a set of interrelated categories, namely human facts, reflective subject, structure, author's worldview, and explanation. The author uses a qualitative research approach, a study that tries to understand the problem holistically in a descriptive way. The steps used in analyzing the poems are: (a) collecting Toto Sudarto poems; (b) reading and understanding Toto Sudarto poetry; (c) analyzing genetic structuralism in Toto Sudarto poetry; (d) describing the results of the analysis. The research results of this article are in the form of poetry analysis which refers to the points; social activity, reflective subject analysis, and dialectical methods. Keywords: genetic structuralism, Lucien Goldman, poetry

Nutritional Status of Under-five Children in Sokoto, Nigeria

Background: Malnutrition is the largest risk factor in the world for disability and premature mortality among young children, especially in developing countries. A myriad of socio-economic, cultural, and environmental factors have been identified to either improve or negatively affect a child's nutritional status. Objectives: To assess the nutritional status of children under-five years of age in Sokoto, Nigeria, and determine the associated factors. Methods: A cross-sectional survey of children aged 6-59 months selected through multistage random sampling. Data was obtained using pretested semi-structured questionnaire and the nutritional indices were determined using WHO anthro v3.2.2. Data was analyzed using SPSS version 25. Results: Subjects comprised a total of 353 children with a mean (±S.D) age of 26.8 ±14.9 months. Majority 214(60.6%) were females. Most 330(93.5%) of the caregivers were married majority 180(51.0%) were of low socio-economic status. Two hundred and two (57.2%) of the under-fives had normal weight for age, 144(40.8%) of them were severely stunted, most 290 (82.2%) of them had normal weight for height, and 199(56.7%) had normal mid upper arm circumference. Mother/caregiver's age (p=0.012), marital status (p= 0.007), and child's immunization status (p=0.015) were associated with weight for age, marital status (p=0.028) was associated with the weight for height/length, Child's age group category (p&lt;0.001), social class (p=0.036), and community setting (p=0.031) were associated with the height for age, Marital status (p=0.025), child's age group category (p&lt;0.001), history of recent illness (p=0.046), and community setting (p&lt;0.001) were associated with the mid-upper arm circumference. Conclusion: Under nutrition was the main form of malnutrition among the under-fives, with severe stunting predominating. The nutritional status were influenced by some maternal or caregivers' socio-demographic factors, as well as inadequate child immunization and treatment of acute illnesses.

Effectiveness of physical therapy of a person with a hemorrhagic stroke during the recovery period: a clinical case

Purpose: to scientifically substantiate and develop a program of physical therapy for a patient after a hemorrhagic stroke (during the recovery period) and check its effectiveness. Material &amp; Methods: the study was conducted on the basis of the "Skalyansky Clinic" Medical Center. The organization of the study involved conducting a comparative analysis of the dynamics of indicators obtained with the help of clinical tools for assessing the patient’s functional state in the process of applying the author’s physical therapy program. A 71-year-old patient with a diagnosis of "Acute cerebrovascular hemorrhagic disorder: subarachnoid-parenchymal hemorrhage with the formation of a stroke-hematoma of the left temporal-subcortical area of the brain with breakthrough into the ventricular system with right-sided hemiplegia, sensorimotor aphasia" by a multidisciplinary team (doctor, physical therapist, speech therapist and psychologist) an individual program of physical therapy was developed. In order to evaluate the effectiveness of the experimental methodology, a formative experiment was conducted. Clinical tools for assessing the functional state of the patient during the research were domains in the form of the ICF-core-set tool (a set of ICF basic categories) (ICF Research Branch, 2022) for complex stroke: Lovett’s test, Barthel scale, Gait Velocity (GV) tests and Berg Balance scale (BBS), Montreal Scale of Cognitive Assessment (MoCA), Get Up and Go Tests, Trunk Control Test, Functional Independence Measure (FIM). The developed individual program of physical therapy for hemorrhagic stroke in the recovery period is a comprehensive course of physical therapy, occupational therapy, which is carried out in stages and includes the following components: therapeutic gymnastics classes; electrical stimulation of the shoulder, psychological (psychological counseling, psychological support, developmental classes for the recovery of cognitive impairments) and speech therapy (speech therapy gymnastics, articulation exercises, Su-Jok (massage ball and ring) component. After applying the specified author’s individual physical therapy program, qualitatively positive changes in the functional and psychophysical state of the patient, in particular: b7301.4 to b7301.2; d550.2 to d550.1; d510.3 to d510.2; d520.3 to d520.2; d540.3 to d540. 2; d598.3 to d598.2; d4100.3 to d4100.2; d450.3 to d450.2; d469.4 to d469.3; d330.3 to d330.1; d355.3 to d355.3; d998.3 to d998.2. Results: after applying the author’s program of physical therapy, we found qualitatively positive changes in the functional and psychophysical state of the patient, as evidenced by the positive dynamics of the studied indicators. The psychological and speech therapy work carried out contributed to the improvement in the process of restoring the patient’s cognitive functions (from moderate to mild impairment), stabilization of his emotional state, acquisition of stress resistance skills, harmonization of the patient’s system of relationships with himself, Conclusions: the effectiveness of using the author’s individual program of physical therapy is confirmed by the dynamics of qualitatively positive changes in the functional and psychophysical state of the patient.

Content-related solution quality in invention activities and worked solutions – promoting the professional vision of classroom management

Invention activities and worked solutions are considered to be effective learning tasks. To date, limited research has been conducted regarding these tasks in teacher education and the process of solving these tasks. This study focuses on the solution quality of student teachers’ task solutions. 149 students were randomly assigned to one of two experimental conditions: invention activity and worked solution. The latter group were given a set of categories; the former group had to invent their own categories to compare two constructed contrasting auditive cases with a focus on the subject classroom management. To determine whether it is more effective to compare cases with given categories (worked solution) or with self-generated categories (invention activity), we coded the 149 solutions regarding the content-related solution quality using qualitative content analysis. Students in the worked solution condition demonstrated a significantly higher content-related solution quality than those in the invention activity condition. Thus, it may be assumed that students of the worked solution gained a better conceptual understanding of classroom management through working on this task. Implications for the use of this task format in teacher education are discussed.