Hierarchical Type Research Articles

Love Affair at Workplace: How Motif and Type Linked to Employee Work Behaviour in Male Employee Experience

Men tends to act more positively than female when it comes to romance in the workplace (Pierce et al., 1996; Powell, 1986). This research presents an exploratory assessment of workplace affairs using a qualitative approach to identify how motives and types of workplace affairs, linked on how it affect employee work behavior from a male employee experience. Social exchange theory and power dynamics approached are used to explain this phenomenon. Respondents were collected using purposive and snowball sampling methods due to the sensitive nature of the respondent criteria. The semi-structured, one-on-one in-depth interview was done to nine male employees who had and/or are engaging in workplace affair. Finding reveals motives and types of workplace affair linked to each other especially ego/fling motive with hierarchical type of relationship. Workplace affair does affect work behavior in vary ways. Additionally, male employee experienced negative effect despite them being superior or subordinate. A negative effect mentioned such as favoritism, power abused even workplace bullying. This study emphasizes more of workplace affair as the dark side of workplace romance thus enriched the findings of previous study regarding workplace affair in Indonesia especially from male employee experience.

Hierarchical classification for improving parcel-scale crop mapping using time-series Sentinel-1 data

Parcel-scale crop classification utilizing time-series satellite observations is of significant importance in precision agriculture. The prior knowledge that crop types can be organized in a hierarchical tree structure is beneficial for improving crop classification. Moreover, the crop hierarchy aligns with the coarse-to-fine cognitive process of geographic scenes. Based on the crop hierarchy, this study developed a general hierarchical classification framework for enhancing crop mapping using time-series Sentinel-1 data. Central to this method is a deep-learning-based hierarchical classification model that explores and makes use of crop hierarchical knowledge. First, preprocessed Sentinel-1 data were geometrically overlaid onto farmland parcel maps to derive parcel-scale time-series features. Second, we constructed a hierarchical crop type system for study areas based on the crop phenology of labeled crop-type samples. Third, we developed a deep-learning-based hierarchical classification model to identify crop types for each parcel, to generate final crop-type classification maps. The proposed approach was further discussed and verified through the implementation of parcel-scale time-series crop hierarchical classifications in a study area in France with farmland parcel maps and time-series Sentinel-1 data. The classification results, indicating significant improvements greater than 4.0% in overall accuracy and 5.0% in F1 score over comparative methods, demonstrated the effectiveness of the proposed method in learning multi-scale time-series features for hierarchical crop classification utilizing Sentinel-1 data sequences.

Functioning and translation of English and Russian architectural terms in academic discourse

The study provides the analysis of architectural terminology in English and Russian in academic discourse. The object of the study is the terminological system of architecture. The subject of the study is the specifics of the use, functioning and translation of English and Russian architectural terminology in academic discourse. The classification and systematization of architectural terms, their hierarchical structure and functional types in academic texts are considered. In addition, the interdisciplinary nature of the architectural sublanguage is emphasized, which intersects with terms from the fields such as engineering, architecture, construction, art and design. The paper also identifies existing difficulties and problems that arise when translating English and Russian-language architectural terms, including differences in the linguistic structure of languages, semantic ambiguity, the need for a high degree of specialization of the translator for the correct interpretation of the term, the use of idiomatic and figurative language to describe some architectural terms, etc. The study uses a comprehensive methodology, in particular, methods of comparative and component analysis, as well as methods of corpus linguistics used to study semantic fields, hierarchical relationships and functional types of terms. The research results emphasize the importance of standardization of architectural terminology, understanding of cultural and historical background, as well as technical knowledge to ensure effective communication in the global architectural community. Analyzing the corpus of texts, including dictionaries, architectural reviews and academic articles, the author explains how specific terms function in academic discourse. This study makes a special contribution to the development of linguistic research in the field of architecture, offering an in-depth analysis of semantic fields and their role in structuring architectural vocabulary. The novelty of the research lies in an integrated approach to the study of the functioning and translation of architectural terms, which allows to identify the difficulties associated with the interdisciplinary character of the terminology. The results of the study will be useful not only for teachers and translators, but also for international experts in the field of architecture.

Chemical entity normalization for successful translational development of Alzheimer’s disease and dementia therapeutics

BackgroundIdentifying chemical mentions within the Alzheimer’s and dementia literature can provide a powerful tool to further therapeutic research. Leveraging the Chemical Entities of Biological Interest (ChEBI) ontology, which is rich in hierarchical and other relationship types, for entity normalization can provide an advantage for future downstream applications. We provide a reproducible hybrid approach that combines an ontology-enhanced PubMedBERT model for disambiguation with a dictionary-based method for candidate selection.ResultsThere were 56,553 chemical mentions in the titles of 44,812 unique PubMed article abstracts. Based on our gold standard, our method of disambiguation improved entity normalization by 25.3 percentage points compared to using only the dictionary-based approach with fuzzy-string matching for disambiguation. For the CRAFT corpus, our method outperformed baselines (maximum 78.4%) with a 91.17% accuracy. For our Alzheimer’s and dementia cohort, we were able to add 47.1% more potential mappings between MeSH and ChEBI when compared to BioPortal.ConclusionUse of natural language models like PubMedBERT and resources such as ChEBI and PubChem provide a beneficial way to link entity mentions to ontology terms, while further supporting downstream tasks like filtering ChEBI mentions based on roles and assertions to find beneficial therapies for Alzheimer’s and dementia.

Neural waves and computation in a neural net model II: Data-like structures and the dynamics of episodic memory.

The computational resources of a neuromorphic network model introduced earlier were investigated in the first paper of this series. It was argued that a form of ubiquitous spontaneous local convolution enabled logical gate-like neural motifs to form into hierarchical feed-forward structures of the Hubel-Wiesel type. Here we investigate concomitant data-like structures and their dynamic rôle in memory formation, retrieval, and replay. The mechanisms give rise to the need for general inhibitory sculpting, and the simulation of the replay of episodic memories, well known in humans and recently observed in rats. Other consequences include explanations of such findings as the directional flows of neural waves in memory formation and retrieval, visual anomalies and memory deficits in schizophrenia, and the operation of GABA agonist drugs in suppressing episodic memories. We put forward the hypothesis that all neural logical operations and feature extractions are of the convolutional hierarchical type described here and in the earlier paper, and exemplified by the Hubel-Wiesel model of the visual cortex, but that in more general cases the precise geometric layering might be obscured and so far undetected.

DEMANDS AND OFFERS OF PRIMARY CARE IN CHILDREN’S MENTAL HEALTH

The objective was to understand the demands and offers of Primary Care with regard to mental health care for children and adolescents, from the perspective of workers. This is a qualitative research, made with 41 primary care (BA) health workers in a municipality in the Southeast region. As an adopted methodology semi-structured interviews were used and two focus groups, in the latter, 20 workers participated among those who were interviewed. This was followed by a lexical analysis of the Descending Hierarchical Classification type with the help of the IRAMUTEQ software, resulting in five classes. Conceptions of childhood and adolescence appeared in the reports, still marked by ideas of incompleteness and a logic of specialized care.

A colorimetric sensor for rapid discrimination of tea polyphenols and tea authentication based on Rh-decorated Pd nanocubes with high peroxidase-like activity

The rapid development of nanozymes has offered substantial opportunities for the fields of biomedicine, chemical sensing, and food safety. Among these applications, multichannel sensors, with the capability of simultaneously detecting multiple target analytes, hold promise for the practical application of nanozymes in chemical sensing with high detection efficiency. In this study, Rh-decorated Pd nanocubes (Pd–Rh nanocubes) with significantly enhanced peroxidase-like activity are synthesized through the mediation of underpotential deposition (UPD) and subsequently employed to develop a multichannel colorimetric sensor for discriminating tea polyphenols (TPs) and tea authentication. Based on a single reactive unit of efficient catalytic oxidation of 3,3′,5,5′-tetramethylbenzidine dihydrochloride (TMB), the nanozyme-based multichannel colorimetric sensor responds to each analyte in as short as 1 min. With the aid of principal component analysis (PCA) and hierarchical cluster analysis (HCA), various TPs and types of tea can be accurately identified. This work not only provides a new type of simply structured and highly active nanozymes but also develops a concise and rapid multichannel sensor for practical application in tea authentication and quality inspection.

Leveraging organizational culture to create competitive value from environmental practices

There has been a long-standing debate in the management literature as to the performance implications of environmental practices. Drawing upon the contingent resource-based view, we hypothesize that organizational culture moderates the relationship between environmental practices and firm performance. We use the competing value framework to explore the effects of developmental, group, rational, and hierarchical culture types on three key capabilities (i.e., stakeholder integration, higher-order learning, and continuous innovation) that stem from implementing environmental practices. We test our hypotheses using data from the fourth round of the High Performance Manufacturing survey. We find that the developmental, group, and rational culture types positively moderate the relationship between external proactive environmental practices and firm performance. By contrast, hierarchical culture negatively moderates the relationship between internal proactive environmental practices and firm performance, but positively moderates the relationship between external reactive environmental practices and firm performance. As such, our study contributes to the literature by unpacking the influence of organizational culture on the capabilities through which environmental practices affect firm performance. Our results can guide operations management executives in choosing the most suitable environmental practices that will align with their organizational culture.

Abstract 7352: ScanCT: A tree-based machine learning model to detect single-cell genomic features associated with clinical outcomes

Abstract Single-cell technologies represent a revolutionary approach to resolving cell-type heterogeneity, identifying cells in specialized states, and detecting rare disease-associated cells. With the cost of single-cell technology decreasing substantially, its integration into clinical studies is gaining momentum. A new computational tool is needed to accommodate different single-cell genomics and clinical data formats while accounting for unwanted confounders. The study aims to develop a tree-based machine learning model to leverage the unprecedented resolution of single-cell multi-omics data for delineating the genomic and phenotypic drivers behind diverse immunotherapy responses. The proposed model is called single-cell analysis of Clinical Tree (scanCT), inspired by the Generalized Unbiased Interaction Detection and Estimation method for unbiased gene and protein feature selection and easy interpretation. The scanCT model learns from the data to select the genomic feature that best splits the cells from distinct clinical responses for each tree node. The confounding factors will be regressors in the nodes but not be used for branch splitting, while gene and protein features of interest will split the tree but not enter the regression model in each node. scanCT is built to be free from the biased selection towards variables of a larger number of categories or values. With tree-pruning and cross-validation, scanCT overcomes the over-fitting issue and enhances model generalization, especially for clinical studies with limited patients. Particularly, scanCT naturally fits the hierarchical cell type relationship and handles marker gene and protein interaction effects efficiently. Our approach was tested on single-cell datasets from B-cell malignancy patients undergoing Chimeric Antigen Receptor (CAR)-T cell therapy. The results from the scanCT are highly interpretable. For instance, each branch is a gene-protein combination profile, and cells are naturally partitioned by clinical association. The linear regressions at each leaf node are the clinical predictions for cells following the splitting criteria. The regression intercept is an average estimation of toxicity (e.g., neurotoxicity) or efficacy after controlling for confounder (e.g., tumor burden). scanCT accommodates categorical or continuous clinical response and survival data and is robust to missing values, a frequent challenge in oncological studies. scanCT represents a significant step forward in single-cell data analysis, which merges complex genotypic and phenotypic information with clinical outcomes. The efficacy and toxicity-associated genomic signatures will inform new manufacturing strategies to optimize CAR-T cell therapy products. The model and clinical association detections are expected to go beyond the B-cell malignancy field to benefit the broader cancer research community. Citation Format: Ye Zheng, Long Nguyen, Peigen Zhou, Alexandre V. Hirayama. ScanCT: A tree-based machine learning model to detect single-cell genomic features associated with clinical outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7352.

A Knowledge and Evaluation Model to Support the Conservation of Abandoned Historical Centres in Inner Areas

The planning of interventions aimed at preserving the built heritage of inner areas is a complex process due to the fragility of these contexts. It should stem from adequate knowledge to support the recognition of qualities, resources, and potentials, and the reinterpretation of residual values. From the perspective of an axiological approach to the built heritage, it is possible to foster the resemantization of such values based on a rigorous cognitive model. This research proposed a cognitive model of the built heritage of the historic neighbourhood of Granfonte in Leonforte (Enna). The knowledge model, developed in Excel, has a hierarchical type of structure characterized by domain, classes, properties, and the attribution of values to properties. The approach makes it possible to execute queries that arise from specific relationships between classes. In this study, we developed both simple queries to measure the percentages of certain characteristics of the building units and complex queries for the estimation of aggregate indices to define the degree of transformation and loss of integrity ITI and degradation ID of the building units or to identify those most exposed to the risk of ruination and contagion. The proposed model can be framed within the framework of ontologies supporting structured heritage knowledge.

Enabling Human-Autonomy Teaming in Aviation: A Framework to Address Human Factors in Digital Assistants Design

The introduction of artificial intelligence (AI) tools in aviation necessitates more research into human-autonomy teaming in these domain settings. This paper describes the development of a design framework for supporting Human Factors novices in considering human factors, improving human-autonomy collaboration, and maintaining safety when developing AI tools for aviation settings. Combining elements of Hierarchical Task Analysis, Coactive Design, and Types and Levels of Autonomy, the design framework provides guidance in three phases: modelling and understanding the existing system and associated tasks; producing a new function allocation for optimal Human-Autonomy Teaming (HAT); and assessing HAT-related risks of the proposed design. In this framework, designers generate a comprehensive set of design considerations to support subsequent development processes. Framework limitations and future research avenues are discussed.

Влияние структуры управления на инновационные возможности организации

The current economic situation, characterized by rapidly changing market rules, requires business structures to respond immediately in order to maintain competitive advantages. The frequently changing requirements of customers, whose priorities are updated as quickly as technological breakthroughs and innovations dictate their consumer conditions to the business. In addition, newly created companies structure their activities in a unique way in accordance with changing customer requirements and challenge traditional corporate structures. The article considers the issues of functioning, development and improvement of organizational management structures of organizations in the implementation of innovations. The features of hierarchical and organic types of management are highlighted. The elements of the organizational structure of the enterprise inherent in any management model are presented. The results of a survey of 148 large Russian enterprises conducted by KPMG consulting company together with the Agency for Strategic Initiatives (ASI) are considered. The author's conclusions based on the results of the study are made. Recommendations for the successful functioning of the organization in the implementation of innovations are proposed.

Subcooled Pool Boiling on Hierarchical Micro- and Nanostructure-Modified Copper Surfaces in HFE-7100 Dielectric Liquid

ABSTRACT Hierarchical surfaces comprised both microscale and nanoscale structures have been previously studied as a means of targeting multiple length scales to achieve superior pool boiling performance. However, preceding studies have focused almost exclusively on high surface tension working fluids, while technologically important low surface tension fluids have remained largely unexplored. Due to their significantly lower surface tension these liquids tend to push out the air trapped in surface cavities of the heating surface, resulting in fewer nucleation sites compared to the same surface in water at low to moderate superheats. Thus, developing effective surface modification techniques for pool boiling in dielectric liquids and understanding the multiphase physics behind them is a pressing need in order to overcome these performance limitations and accelerate their adoption. In this work, we utilize scalable manufacturing techniques to realize four separate surface types (planar, nanoscale-modified, microscale-modified, and hierarchical) and experimentally determine their respective pool boiling performance within the low surface tension commercial working fluid HFE-7100. A maximum heat transfer enhancement of 125% at 38 K of superheat was observed for the best performing samples, which interestingly were nanoscale-modified and not those of the hierarchical type. Visual observations via high-speed video analysis of vapor bubble behavior are utilized to explain the underlying multiphase physics as to why these samples performed so well and future directions for achieving surface optimization across multiple length scales.

Enzymatic dual-faced Janus structures based on the hierarchical organic-inorganic hybrid matrix for an effective bioremoval and detoxification of reactive blue-19

A novel, dual-faced, and hierarchical type of Janus hybrid structures (JHSs) was assembled through an in situ growing of lipase@cobalt phosphate sheets on the laccase@copper phosphate sponge-like structures. The chemical and structural information of prepared JHSs was investigated by Scanning electron microscopy-energy dispersive X-ray analysis (SEM-EDX), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray diffraction analysis (XRD). The catalytic activity, storage stability, and reusability of JHSs were then investigated. The SEM-EDX analysis clearly confirmed the asymmetric morphology of the fabricated JHSs with two distinct metal distributions. Under optimized synthesis conditions, the prepared JHSs showed 97.8 % and 100 % of laccase and lipase activity, respectively. Compared to the free biocatalysts, the immobilization resulted in ~ a 2-fold increase in laccase and lipase stability at temperatures of >40 °C. The fabricated JHSs maintained 61 % and 90 % of their original laccase and lipase activity upon 12 successive repetition cycles. Up to 80 % of Reactive Blue-19 (RB-19), an anthraquinone-based vinyl sulphone dye, was removed after 5 h treatment with the prepared JHSs (50 % higher than the free forms of laccase and lipase). The dye removal data fitted very well on the pseudo-second-order kinetic model with a rate constant of 0.8 g mg−1 h−1. Following the bioremoval process, bacterial toxicity also decreased by about 70 %. Therefore, the prepared JHSs provide a facile and sustainable approach for the decolorization, biotransformation, and detoxification of RB-19 by integrating enzymatic oxidation and hydrolysis.

Information theory-based direct causality measure to assess cardiac fibrillation dynamics.

Understanding the mechanism sustaining cardiac fibrillation can facilitate the personalization of treatment. Granger causality analysis can be used to determine the existence of a hierarchical fibrillation mechanism that is more amenable to ablation treatment in cardiac time-series data. Conventional Granger causality based on linear predictability may fail if the assumption is not met or given sparsely sampled, high-dimensional data. More recently developed information theory-based causality measures could potentially provide a more accurate estimate of the nonlinear coupling. However, despite their successful application to linear and nonlinear physical systems, their use is not known in the clinical field. Partial mutual information from mixed embedding (PMIME) was implemented to identify the direct coupling of cardiac electrophysiology signals. We show that PMIME requires less data and is more robust to extrinsic confounding factors. The algorithms were then extended for efficient characterization of fibrillation organization and hierarchy using clinical high-dimensional data. We show that PMIME network measures correlate well with the spatio-temporal organization of fibrillation and demonstrated that hierarchical type of fibrillation and drivers could be identified in a subset of ventricular fibrillation patients, such that regions of high hierarchy are associated with high dominant frequency.

Stability analysis of sampled-data systems via affine canonical Bessel-Legendre inequalities

The looped-functional approach has been extensively used for stability analysis of sampled-data systems. For this approach, this paper introduces two new functionals to minimise the conservatism of stability conditions. The first one is called a non-negative integral functional (NIF) that is added to the derivative of a common Lyapunov functional. The second one is a general looped-functional (GLF), whose values at sampling instants are traditionally not well-defined, and are defined here in terms of their limits. Leveraging further affine canonical Bessel-Legendre inequalities expressed by simplified polynomials, a polynomial-based NIF, a polynomial-based GLF, and a polynomial-based zero equality are tailored for obtaining hierarchical types of stability conditions in the form of linear matrix inequalities. Finally, numerical examples show that: (i) the proposed functionals lead to less conservatism when compared with several state-of-the-art methods; and, (ii) the resulting stability conditions exhibit a hierarchical characteristic in the sense that the higher level of hierarchy, the less conservatism of the criteria.

The model of rational stimulation of project team members based on game theory tools

This work is devoted to the development of recommendations for managing a project team at the conflict stage of its formation using game theory tools. In the course of the study, the main approaches to modeling the activities of the project team were described, as well as the features of the process of its formation were considered in detail. To model business activity at the conflict stage of team formation, a game-theoretic model of a hierarchical type was selected and adapted. The constructed model of team stimulation, as a kind of hierarchical game, allows us to consider the interaction of the management center both with the team as a whole and with individual participants. The adapted model is used in the task of finding a common team solution. Unlike the basic model, the adapted model does not use the type of each of the team members, which significantly reduces the amount of information needed for calculations, and also simplifies the calculations themselves. At the same time, information about the complexity of the task, the relationship between team members, as well as the priority of the task assigned to the participants was added to the basic model. In comparison with the empirical decision-making on incentives, the developed model allows the management center not only to achieve the tasks assigned to the project team with guarantee and on time, but also to use the company's monetary resources rationally.

Пример алгоритма для расчёта специального лекарственного севооборота

The article presents a description of the algorithm in Word Excel format for calculating the structure of the small-scale field experiments areas as part of special medicinal crop rotation on the example of the VILAR experimental field (Moscow region) according to data for the period 2011-2021. The programming of the algorithm is implemented on the basis of conditional and logical operators. In the comments to the algorithm is given: a description of the hierarchical structure, criteria, types and relationships of data, the scheme of the algorithm, data features. The illustrations show the screen view of the algorithm, input and output data, the structure and location of data in the calculation table at different stages of using the algorithm. This algorithm is applicable for a system most similar to a crop rotation system with separate working areas within a crop rotation field of variable size. The results are relevant in the field of applied information technologies and scientific applied research for use in agricultural production in order to improve the efficiency of land use. Keywords: APPLIED INFORMATION TECHNOLOGIES, BRANCHING ALGORITHM, STRUCTURE OF CROP ROTATION AREAS, FIELD EXPERIENCE

Noodle Grouping Based on Nutritional Similarity with Hierarchical Cluster Analysis Method

Everyone in the world definitely needs nutritious food. Therefore consumers must be smart in choosing nutritious foods. Noodles are a type of food that is popular in Indonesia. The purpose of this study was to classify fourteen types of noodle brands based on the similarity in the amount of nutritional content in each component. The method used in this study is a hierarchical cluster type analysis method with the SPSS software tool. The results showed that the fourteen types of noodles can be classified into clusters between two and four. Classification into two clusters shows that there are four types of noodle brands that are nutritionally similar in one cluster A and there are ten types of noodle brands that are similar in nutritional content in one cluster B.

How Advanced Practice Nurses Can Be Better Managed in Hospitals: A Multi-Case Study

The labor markets for care professionals are a perennial topic of discussion. Advanced practice nurses (APN), as an advanced role in nursing, arose to solve the shortage of primary health care professionals. Prior research has advanced several areas of exploration for APNs' training or employing methods in Chinese hospitals. However, this leaves a key imperative unexplored: the management strategy of APNs in hospitals. The present study seeks to explore the management strategy of APNs in Chinese hospitals. The resource orchestration theory served as the guide as the multi-case study method investigated 18 case hospitals, gathered information from a variety of case data sources, and summarized the management strategies for hospitals' advanced practice nurses. Four types of APN management strategies-expert customized type, hierarchical linkage type, multidisciplinary benefit type, and professional penetration type-have been identified through resource orchestration. Hospitals can utilize the APN management strategy model as a guide to manage APNs in accordance with the unique characteristics of APN resources.