Simple Categorization Research Articles

Abstract task sequence initiation deficit dissociates anxiety disorders from obsessive-compulsive disorder and healthy controls.

In everyday life, humans perform sequences of tasks. These tasks may be disrupted in people with obsessive-compulsive disorder (OCD). Symptoms, such as compulsions, can be considered sequential and often cause repetitions of tasks that disrupt daily living (e.g., checking the stove while cooking). Motor sequences have been used to study behavioral deficits in OCD. However, not all sequences are motor sequences. Some are more "abstract" in that they are composed of a series of tasks (e.g., chopping and stirring) rather than being dependent on individual actions or stimuli. These abstract task sequences require cognitive control mechanisms for their execution. Although theory has proposed deficits in these sequences in OCD as well, they have not been directly investigated. We tested the hypotheses that OCD participants exhibit deficits in the control mechanisms specific to abstract task sequences and more general flexible behavior (measured with task switching within the sequences), relative to health controls (HCs) and clinical controls (participants with anxiety disorders [ANX]). A total of 112 participants completed abstract task sequences consisting of simple categorization tasks. Surprisingly, participants with OCD did not perform worse than HCs or ANX. However, ANX participants showed impairments specific to sequential control that did not extend to more general flexible control. Thus, we showed a novel behavioral dissociation between OCD and ANX specific to abstract task sequential control. These results also implicate deficits in specific frontal sequential control neural circuitry in ANX and not in OCD, where implicit sequential deficits may more closely align with striatal circuits.

Equilibrium curves and modeling of binary systems for the carbon di-oxide + benzyl acetoacetate and carbon di-oxide + benzyl acetate mixtures under high pressure

The solution phase behavior of the binary systems of supercritical carbon di-oxide (SU-CO2) + benzyl acetoacetate and SU-CO2 + benzyl acetate was investigated in a synthetic high-pressure apparatus at five temperatures from 313.2 to 393.2 K and pressure up to 33.53 MPa for the industrial benefit of food, pharmaceutical and cosmetics application. The solubility of benzyl acetoacetate and benzyl acetate in the SU-CO2 + benzyl acetoacetate and SU-CO2 + benzyl acetate systems were increased with increasing temperature at constant pressure, respectively. Both system isotherms were exhibited in the simple Type-I category phase behavior. Besides, the Peng-Robinson equation of state has been successfully applied to predict the phase behavior of the SU-CO2 + benzyl ester systems using adjustable molecular interaction parameters (kij and ηij). Neither system shows a three-phase behavior at any point of temperature and pressure. A one-fluid-phase locale was ascertained above and throughout the solubility curve whereas a two-phase locale was exhibited inside the critical curve for both binary systems. The critical mixture curve provides the fingerprint for the phase behavior study of any binary system since it is used to understand and calculate thermodynamic properties effectively. The accuracy of the studied model was tested by evaluating the percentage of root mean square deviation utilizing optimized temperature-dependent mixture parameters. Indeed, this is the first reference point for the prediction of phase transition behavior for benzyl acetoacetate and benzyl acetate in SU-CO2 and the findings make a remarkable impression on industrial applications.

System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.

Comparative analysis and prospect of RNN and Transformer

Deep learning techniques have emerged as a key catalyst for innovation in the rapid advancement of artificial intelligence and machine learning. Deep learning has significantly transformed the field of natural language processing (NLP) and voice processing. It has revolutionized the processing and comprehension of linguistic data, leading to advancements in various applications, ranging from simple text categorization to intricate speech recognition. The progress made in this area would not have been feasible without the utilization of two crucial neural network models: the Recurrent Neural Network (RNN) and the Transformer. These models, with their distinctive processing skills, have attained significant accomplishments in the domains of NLP, computer vision, and several other sectors. Nevertheless, although they demonstrate exceptional performance in their specific application scenarios, there are notable distinctions in processing techniques, performance enhancement, and application range. This study aims to thoroughly investigate and compare the efficacy of two models, namely RNN and Transformer, in processing natural language and speech data. This will be achieved using the research methods of literature analysis and literature review. Through the analysis of their structures, strengths, flaws, and performance in practical applications, it offers a more comprehensive viewpoint and benchmark for future study and applications. As technology advances, we anticipate the emergence of more novel models and methodologies in the field of natural language processing and voice processing. This will further facilitate the development of these technologies.

Variability in the prevalence of inappropriate medication use among older adults: A review highlighting the importance of screening methods and database types.

The global older population is growing rapidly, and the rise in polypharmacy has increased potentially inappropriate medication (PIM) encounters. PIMs pose health risks, but detecting them automatically in large medical databases is complex. This review aimed to uncover PIM prevalence in individuals aged 65 years or older using health databases and emphasized the risk of underestimating PIM prevalence due to underutilization of detection tools. This study conducted a broad search on the Medline database to identify articles about the prevalence of PIMs in older adults using various databases. Articles published between January 2010 and June 2023 were included, and specific criteria were applied for study selection. Two literature reviews conducted before our study period were integrated to obtain a perspective from the 1990s to the present day. The selected papers were analysed for variables including database type, screening method, adaptations and PIM prevalence. The study categorized databases and original screening tools for clarity, examined adaptations and assessed concordance among different screening methods. This study encompassed 48 manuscripts, covering 58 sample evaluations. The mean prevalence of PIMs within the general population aged over 65 years was 27.8%. Relevant heterogeneity emerged in both the utilized databases and the detection methods. Adaptation of original screening tools was observed in 86.2% (50/58) of cases. Half of the original screening tools used for assessing PIMs belonged to the simple category. About a third of the studies employed less than half of the original criteria after adaptation. Only three studies used over 75% of the original criteria and more than 50 criteria. This extensive review highlights PIM prevalence among the older adults, emphasizing method intricacies and the potential for underestimation due to data limitations and algorithm adjustments. The findings call for enhanced methodologies, transparent algorithms and a deeper understanding of intricate rules' impact on public health implications.

EmoSonics: Emotion Detection via Voice and Speech Recognition

Understanding emotions from speech is like deciphering a rich tapestry of human expression in the realm of human-computer interaction. It's akin to listening to someone's tone and inflection to discern whether they're happy, surprised, or experiencing a range of other feelings. Researchers use a variety of techniques, from analyzing speech patterns to utilizing advanced technologies like fMRI, to decode these emotional cues. Emotions aren't just simple labels; they're complex and nuanced, demanding sophisticated methods for accurate interpretation. Some methods break emotions down into simple categories, while others embrace the intricacies of human emotion, treating them as continuous variables. Ultimately, the goal is to enhance communication between humans and computers by enabling machines to understand and respond appropriately to our emotional states. This pursuit underscores the significance of emotion detection in speech analysis and highlights the need for continually evolving methodologies in human-computer interaction research.

Insights Unveiled: Harnessing AI to Explore Human Behaviour in Social Sciences

AI's evolution has expanded its potential to understand complex human behaviours, revealing new insights into human psychology and sociology. This essay explores AI's capabilities in detecting intricate behavioural patterns, its applications in social sciences, the challenges and ethical considerations associated with its use, and predictions for the future. AI's ability to uncover patterns invisible to the human mind offers an exciting frontier of exploration, particularly in understanding human behaviour. Human behaviour, characterized by its intricacy and influenced by sociocultural, economic, and psychological factors, has traditionally been studied through various methodologies. However, the unpredictability and dynamism of human behaviour often defy simple categorization or comprehension. AI has the potential to serve as a guide, decoding patterns beyond the reach of human cognition and traditional methodologies. AI can offer a new lens through which we view the rich tapestry of human experience, such as predicting social trends, understanding consumer behaviour, and exploring the underpinnings of political sentiment. Despite challenges such as feasibility, interpretability, and ethics, the potential impact of AI in understanding human behaviour is enormous. By shedding light on hidden patterns, we can foster a more nuanced understanding of our societies, inform policies, drive innovation, and potentially address some of the pressing challenges of our times.

Captain Ahab as a Hero in Melville’s Moby-Dick

Herman Melville’s Moby-Dick tells the story of Ishmael, a sailor who joins the whaling ship Pequod, captained by the obsessed and vengeful Captain Ahab. Ahab’s sole mission is to hunt down and kill the legendary white whale, Moby-dick, who had previously bitten off Ahab’s leg at the knee. As a towering masterpiece of American literature, Moby-Dick is a novel that defies simple categorization and is renowned for its rich narrative complexity and profound thematic depth. Central to the novel is the character of Captain Ahab, whose quest to vanquish the white whale, Moby-dick, forms the back bone of the story. On the one hand, Ahab embodies various heroic qualities; on the other, Ahab is also perceived as a monomaniacal villain due to his apparent flaws and descent into madness. Through an examination of Ahab’s actions, motivations and interactions, this essay argues that Ahab’s heroism is multifaceted, encompassing elements of classical heroism as well as tragic heroism by exploring Ahab’s heroic traits and their implications in the broader context of Melville’s masterpiece.

Site-specific reactivity of stepped Pt surfaces driven by stress release.

Heterogeneous catalysts are widely used to promote chemical reactions. Although it is known that chemical reactions usually happen on catalyst surfaces, only specific surface sites have high catalytic activity. Thus, identifying active sites and maximizing their presence lies at the heart of catalysis research1-4, in which the classic model is to categorize active sites in terms of distinct surface motifs, such as terraces and steps1,5-10. However, such a simple categorization often leads to orders of magnitude errors in catalyst activity predictions and qualitative uncertainties of active sites7,8,11,12, thus limiting opportunities for catalyst design. Here, using stepped Pt(111) surfaces and the electrochemical oxygen reduction reaction (ORR) as examples, we demonstrate that the root cause of larger errors and uncertainties is a simplified categorization thatoverlooks atomic site-specific reactivity driven by surface stress release. Specifically, surface stress release at steps introduces inhomogeneous strain fields, with up to 5.5% compression, leading to distinct electronic structures and reactivity for terrace atoms with identical local coordination, and resulting in atomic site-specific enhancement of ORR activity. For the terrace atoms flanking both sides of the step edge, the enhancement is up to 50 times higher than that of the atoms in the middle of the terrace, which permits control of ORR reactivity by either varying terrace widths or controlling external stress. Thus, the discovery of the above synergy provides a new perspective for both fundamental understanding of catalytically active atomic sites and design principles of heterogeneous catalysts.

Black British love matters: Asserting the transformative power of love in Bolu Babalola’s Love in Colour: Mythical Tales Around the World Retold

ABSTRACT The genre of romance has until fairly recently predominantly depicted white protagonists and presented readers with limited representations of ethnic and racial diversity. This paper provides an overview of the ever-increasing number of Black British authors who are currently addressing this significant gap in the British publishing industry and analyse Bolu Babalola’s debut Love in Colour: Mythical Tales Around the World Retold (2020) as a case in point. Love in Colour is a collection of short stories which questions the universality of western tales of love and celebrates Black love and heritage. My contention is that Babalola’s collection, for its innovative elements, not only evinces that contemporary Black British women’s writing, characterized by aesthetic and formal innovations, continues to “test simple generic categories”, but, in its celebration of the transformative power of love, also demonstrates that Black love matters.

Structural and semantic features of adjectives across languages and registers

Abstract This study focuses on the seemingly simple grammatical category of adjectives. However, the framework adopted for analysing adjectives from both a cross-linguistic and a cross-register perspective highlights features of grammatical complexity within phrases as well as contrastive complexity within and across languages and registers. Using English-Norwegian comparable data from the fictional sub-registers of narrative and dialogue, the analysis uncovers both similarities and differences in the structural and semantic features of adjectives. Most notable is perhaps the impact of register at all levels of analysis, including adjectival function (attributive vs. predicative), the degree of phrase complexity and semantic class. The contrastive analysis proper, i.e. comparing adjective use across languages, reveals that English and Norwegian have more in common within each sub-register than across them. For example, the lexico-grammatical behaviour of adjectives in English narrative has more in common with Norwegian narrative than it has with English dialogue. To the extent that there are clear general differences between English and Norwegian, these are mainly found in the proportions and preferences of a few specific features.

A generalization of the Davis–Moussong complex for Dyer groups

A common feature of Coxeter groups and right-angled Artin groups is their solution to the word problem. Matthew Dyer introduced a class of groups, which we call Dyer groups, sharing this feature. This class includes, but is not limited to, Coxeter groups, right-angled Artin groups, and graph products of cyclic groups. We introduce Dyer groups by giving their standard presentation and show that they are finite-index subgroups of Coxeter groups. We then introduce a piecewise Euclidean cell complex \Sigma which generalizes the Davis–Moussong complex and the Salvetti complex. The construction of \Sigma uses simple categories without loops and complexes of groups. We conclude by proving that the cell complex \Sigma is \mathrm{CAT}(0) .

A Study on the Correlation Between LIWC Word Categories and Chinese Composition Writing Performance of Fourth, Fifth, and Sixth Grade Students

This study focuses on the analysis of the Chinese composition writing performance of fourth, fifth, and sixth grade students in 16 selected schools in Longhua District, Shenzhen during the spring semester of 2023. Using LIWC (Linguistic Inquiry and Word Count) as a text analysis tool, the study explores the impact of LIWC categories on writing performance which is scaled by score. The results show that the simple LIWC word categories have a significant positive influence on the composition scores of lower-grade students; while complex LIWC word categories have a significant negative influence on the composition scores of lower-grade students but a significant positive influence on the composition scores of higher-grade students. Process word categories have a positive influence on the composition scores of all three grades, but the impact of complex process word categories increases as the grade level rises.

Adjusted Purchase Price as A Method of Valuing Financial Assets and Liabilities

The development of accounting in modern times is accompanied by the emergence of new valuation methods. Today, assets and liabilities are disclosed not only in simple categories, such as purchase price, nominal value or production cost. National and international regulations introduce more complex valuation methods. One of them is the adjusted purchase price method, known in international regulations as the amortized cost method. The following article will present the essence of the adjusted purchase price method, the assets and liabilities that can be valued according to this method, as well as a comparison of the discussed valuation method with accounting principles.

Trauma and War: The Psychological Implications for Survivor-victims in Iraqi Fiction

War-time literature about Iraq reflects the terror and the trauma that the civilian population in Iraq have had to live with in recent years. As conflicts and wars overtook the country in a rapid fashion and the state’s hold weakened, the country plunged into total chaos. The infrastructure crumpled, the economy collapsed, and multiple rebel groups surfaced halting peace process in the country. The present paper examines the fictional text, The Sirens of Baghdad, to understand the civilian psychological trauma resulting from repeated wars on Iraqi soil. The gruesome deaths and uncertainty about country’s future have contributed to an overall feeling of dejection and apathy among the civilian population. This research paper concludes that the psychological trauma of the civilian population belies simple categorization like PTSD, and there is a need to determine the true scale of psychological damage suffered over years of exposure to terror.

Challenging dominant narratives, interrupting objectification, and queer creativity: Queer sex worker art in Los Angeles

Drawing on queer of color critique, this paper uses mixed methods including participant observation, interviews, visual and textual analysis, and photovoice to interrogate sex workers’ queer creative practices. Building upon the larger oeuvre of sex working artists, contemporary sex working artists in Los Angeles utilizes queer creativity to thwart hegemonic readings of sex work. Performances at two activist fundraisers drew on the themes and esthetics of sex work to counter mainstream narratives about sex work and workers, and to interrupt their objectification. The cover and graphics of a sex worker zine push back against dominant narratives about sex workers and the power structures that suppress sex worker self-representation. Photovoice methodology allowed sex workers to counteract objectification by telling their own stories. In their creative products, sex workers show how “queer” is a praxis of sex and gender disruption, rather than a simple identity category signaling non-heterosexuality, challenging homonormativity in addition to heteronormativity.

Bodily cues of sex and emotion can interact symmetrically: Evidence from simple categorization and the garner paradigm.

Although much research has investigated how multiple sources of social information derived from faces are processed and integrated, few studies have extended this investigation to bodies. The current study addressed this gap by investigating the nature of the interaction between bodily cues of sex and emotion. Using the Garner paradigm, participants recruited from a university student participant pool categorized the sex or the emotional expression (happy and angry in Experiment 1 [n = 194], angry and sad in Experiment 2 [n = 129]) of bodies across two block types. In orthogonal blocks, participants categorized bodies along one dimension while the second dimension was varied (e.g., categorizing the sex of happy and angry bodies), whereas in control blocks the second dimension was held constant (e.g., categorizing the sex of only happy bodies). Responses were analyzed in two ways. Comparing response times across blocks revealed Garner interference (overall faster categorization in the control than in the orthogonal block) of sex on emotion perception and emotion on sex perception in Experiment 1 but not Experiment 2. Comparing condition level responses in orthogonal blocks indicated that sex cues moderated emotion categorization and emotion cues moderated sex categorization in both experiments. A symmetrical interaction between bodily sex and emotion cues can be observed in simple categorization as well as in the Garner paradigm, though the presence of Garner interference depended on the valence of the expressions used. Given similar results observed in face processing, finding interactivity in body perception is likely to generalize beyond the study sample. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Comparison of aortic blood flow rotational direction in healthy volunteers and patients with bicuspid aortic valves using volumetric velocity-sensitive cardiovascular magnetic resonance imaging.

The rotational direction (RD) of helical blood flow can be classified as either a clockwise (RD+) or counter-clockwise (RD-) flow. We hypothesized that this simple classification might not be sufficient for analysis in vivo and a simultaneous existence of RD+/- may occur. We utilized volumetric velocity-sensitive cardiovascular magnetic resonance imaging (4D flow MRI) to analyze rotational blood flow in the thoracic aorta. Forty volunteers (22 females; mean age, 41±16 years) and seventeen patients with bicuspid aortic valves (BAVs) (9 females; mean age, 42±14 years) were prospectively included. The RDs and the calculation of the rotating blood volumes (RBVs) in the thoracic aorta were performed using a pathline-projection strategy. We could confirm a mainly clockwise RD in the ascending, descending aorta and in the aortic arch. Furthermore, we found a simultaneous existence of RD+/RD-. The RD+/--volume in the ascending aorta was significantly higher in BAV patients, the mean RD+/RD- percentage was approximately 80%/20% vs. 60%/40% in volunteers (P<0.01). The maximum RBV always occurred during systole. There was significantly more clockwise than counter-clockwise rotational flow in the ascending aorta (P<0.01) and the aortic arch (P<0.01), but no significant differences in the descending aorta (P=0.48). A simultaneous occurrence of RD+/RD- indicates that a simple categorization in either of both is insufficient to describe blood flow in vivo. Rotational flow in the ascending aorta and in the aortic arch differs significantly from flow in the descending aorta. BAV patients show significantly more clockwise rotating volume in the ascending aorta compared to healthy volunteers.

The Influence Of Job Satisfaction, Career Development And Teamwork On Employee Retention In Government-Owned Cultural Destinations

The purpose of this study is to determine whether job happiness, teamwork, and career advancement have an impact on employee retention. 100 employees made up the study's population. Probability sampling with a simple random sampling category of 60 participants was the sampling technique employed in this investigation. To gather data, the researcher provided a questionnaire. Path analysis is used in this study. According to research findings, career growth has an impact on workers' job happiness. Teamwork has an impact on workers' job happiness. Employee job happiness is simultaneously influenced by career growth and teamwork. Employee retention is affected by career advancement. Employee retention is influenced by teamwork. Employee retention is influenced by job satisfaction. Teamwork, professional growth, and job happiness all have an impact on employee retention. Job satisfaction, which is influenced by career advancement, affects employee retention. Through increased job satisfaction, teamwork affects employee retention.

The enriched Thomason model structure on 2-categories

We prove that categories enriched in the Thomason model structure admit a model structure that is Quillen equivalent to the Bergner model structure on simplicial categories, providing a new model for (∞,1)-categories. Along the way, we construct model structures on modules and monoids in the Thomason model structure and prove that any model structure on the category of small categories that has the same weak equivalences as the Thomason model structure is not a cartesian model structure. This paper is also available as arXiv:2208.02954v4.