Holistic Classification Research Articles

A classification scheme for urban agriculture combining technical properties with characteristics related to the economic and social sustainability

Urban agriculture is often associated with sustainable agricultural practices. However, the variety of systems qualifying as urban agriculture and the limited information available about their sustainability question this direct relationship. To better understand differences in intra-urban agriculture systems and their sustainability, this paper proposed an holistic classification of urban agricultural systems and collected knowledge about the environmental, social, and economic sustainability of these systems. Such a classification is important to evaluate sustainability claims on urban agricultural systems, anticipate potential sustainability trade-offs between urban agricultural systems and propose preventive measures to address these, and ultimately guide the sustainable deployment of these systems. Compared with existing classifications, the novel classification scheme proposed here accounts for technological, social and economic characteristics of urban agriculture systems to better distinguish between all systems. It was built on 91 scientific papers. The economic intensity of production was, for example, an important characteristic to coherently group urban agriculture systems. The intensity of cooperation between all actors was another characteristic emphasized for certain urban agriculture systems. One end of the classification scheme describes ground-based open, socially motivated urban agriculture systems with high cooperation intensity and low production intensity. The other end of the classification scheme describes building-integrated quasi-closed systems with high production intensity. In between, we find: building-integrated conditioned systems, ground-based conditioned systems, and building-integrated open systems. Mapping sustainability claims from literature in the classification scheme supported its definition along the three characteristics. For example, urban farming was associated with job creation, food safety, water savings, and higher yields while urban gardening with educational potentials, biodiversity improvements, and lower yields. Their display in the classification scheme was therefore supported. To further support the use of the proposed scheme, additional quantitative research to better understand and quantify the sustainability of urban agriculture systems is required.

Global coastal wave storminess

Coastal wave storms pose a massive threat to over 10% of the world’s population now inhabiting the low elevation coastal zone and to the trillions of $ worth of coastal zone infrastructure and developments therein. Using a ~ 40-year wave hindcast, we here present a world-first assessment of wind-wave storminess along the global coastline. Coastal regions are ranked in terms of the main storm characteristics, showing Northwestern Europe and Southwestern South America to suffer, on average, the most intense storms and the Yellow Sea coast and the South-African and Namibian coasts to be impacted by the most frequent storms. These characteristics are then combined to derive a holistic classification of the global coastlines in terms of their wave environment, showing, for example, that the open coasts of northwestern Europe are impacted by more than 10 storms per year with mean significant wave heights over 6 m. Finally, a novel metric to classify the degree of coastal wave storminess is presented, showing a general latitudinal storminess gradient. Iceland, Ireland, Scotland, Chile and Australia show the highest degree of storminess, whereas Indonesia, Papua-New Guinea, Malaysia, Cambodia and Myanmar show the lowest.

Classification of values in accounting and reporting

For proper accounting and reporting, a clear understanding of the values that can be applied in these areas is necessary. Such an understanding certainly gives the classification of values in accounting and reporting. At the same time, as our studies have shown, today there is no single holistic classification of these values. This article is devoted to the creation of such a classification. As research methods, we used the following: analysis and synthesis, grouping method, comparison, analogy method, logical approach, systematic approach. As a result of the research, we have proposed a comprehensive classification that covers all the most important aspects of values in accounting and reporting (including forty-five classification features), meeting the requirements of modern times. The results presented in this paper will be useful to a wide range of readers interested in various problems of modern accounting and reporting, including the values of accounting objects and reporting elements. The results obtained in this article can also be used in the educational process of higher educational institutions and in the practical work of accounting departments. In addition, the results of this study can be used in the development and improvement of relevant regulatory documents on accounting and reporting.

Comparative review and evaluation of state-of-the-art photovoltaic cooling technologies

Photovoltaic systems have a reliable method to generate electrical power from direct solar irradiance. Their efficiency is only up to 20% in real operating circumstances and is affected adversely by the inevitable temperature rise in their surface. Passive, active or combined cooling methods are applied to reduce operating temperature to a suitable level, depending on specific application. A Great body of literature has been investigating different PV cooling methods and techniques to reach optimized system efficiency and cooling effect. It is critical for both researchers and practitioners to have a thorough understanding and awareness of the most recent advances and existing gaps. Although there are certain review papers in this field, it is necessary to have up-to-date reviews of the most recent progress and research since the previous ones. In the present work, the main focus was to provide a review of the most recent studies and to compare their outcomes to provide a reference for practitioners and researchers. The holistic classification and evaluation of all the possible cooling technologies were provided in order to draw a comparison between various cooling approaches. Based on the analysis obtained, cooling techniques can ensure improvement in electrical efficiency in a range from 1% to 53% with expected decrease in the photovoltaic panel's surface operating temperature from 1.8 °C to 46 °C. It was also found that spectrum filtering and jet impingement indicated highly remarkable enhancements. The other factors, such as the size of the examined photovoltaic panel, photovoltaic technology, as well as climate circumstances have a strong impact on the cooling performance besides the selection of the specific coolant. The necessity for integral evaluation (performance, economic and environmental) of cooling techniques was also identified as key issue.

Feasibility analysis of machine learning for performance-related attributional statements

We investigate the feasibility of machine learning methods for attributional content and framing analysis in corporate reporting. We test the performance of five widely-used supervised machine learning classifiers (naïve Bayes, logistic regression, support vector machines, random forests, decision trees) in a top-down three-level hierarchical setting to (1) identify performance-related statements; (2) detect attributions in these; and (3) classify the content of the attributional statements. The training set comprises manually coded statements from a corpus of management commentary reports of listed companies. The attributions include both intra- and inter-sentential attributional statements. The results show that for both intra- and inter-sentential attributions, F1-scores of our most accurate classifier (i.e., support vector machines) vary in the range of 76% up to 94%, depending on the identification, detection and classification levels and the content characteristics of attributions. Additionally, we assess the hierarchical performance of classifiers, providing insights into a more holistic classification process for attributional statements. Overall, our results show how machine learning methods may facilitate narrative disclosure analysis by providing a more efficient way to detect and classify performance-related attributional statements. Our findings contribute to the accounting and management literature by providing a basis for implementing machine learning methodologies for research investigating attributional behavior and related impression management.

ЧИННИКИ ЗАБЕЗПЕЧЕННЯ ФІНАНСОВОЇ СТІЙКОСТІ УНІВЕРСИТЕТІВ В УМОВАХ ПАНДЕМІЇ ТА ВІЙСЬКОВОЇ АГРЕСІЇ РФ

The issue of identification and systematization of factors to ensure the financial stability of universities is due to the transformations and a new paradigm of the higher education institutions' activities. Covid-19 crisis, reduction of budget funding, a decrease of the students’ enrollment, change of the forms of the learning process, requests of potential applicants for educational services, lack of income by universities in a pandemic, ability to adapt and maintain educational and scientific activities during uncertainty are tools to ensure the financial stability of universities. The aim of the article is to study the impact factors and to determine the classification features of the financial stability of the university by systematizing and explaining them. The research was carried out using a dialectical method to substantiate discussion approaches to determining the factors of financial stability of the higher education institutions; a method of analysis to identify and determine the influence of factors in accordance with the classification features; synthesis method for combining certain features and factors influencing financial stability into a holistic classification. The main scientific result of the study is a holistic classification of factors to ensure the financial stability of universities according to certain features. Preliminary research in this direction and views of the scientific community are the scientific basis for identifying the factors of financial stability of the higher education institutions. The classification contains the distribution of factors based on the following: depending on the duration – current and strategic; regarding the nature of the impact – external and internal; by the degree of impact on the financial result – incomes and expenses; concerning the components of the economic model – factors of direct and indirect influence, distributed in relation to the value proposition – key resources, key activities, key partners, stakeholders, channels of supply and promotion, financial model. Significant influence on the financial stability of the university is exerted by behavioral or indirect factors, in particular: image, reputation; stimulating (marketing); institutional; managerial. Systematization of factors according to the proposed classification is a tool for making managerial decisions to ensure the current and strategic financial stability of the university.

Developing a System of Indicators for Clustering Financial Cybercrime

This study aims to propose a system of indicators for dividing financial cybercrime into groups. The results of this study indicate absence of taxonomic indicators regarding clustering of financial cybercrimes and wide differences in approaches to the development of such indicators in relation to cybercrimes. The proposed system of indicators for clustering of financial cybercrime is designed to help create a unified, consistent and holistic classification of cybercrime. The results of the study can be applied in the criminal legal classification of unlawful acts corresponding to the general concept of financial cybercrime.

S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification

Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time. Traditional OSC methods usually train discriminative or generative models with the owned in-class data, and then utilize the pre-trained models to classify test data directly. However, these methods always suffer from the embedding confusion problem, i.e., partial out-of-class instances are mixed with in-class ones of similar semantics, making it difficult to classify. To solve this problem, we unify semi-supervised learning to develop a novel OSC algorithm, S2OSC, which incorporates out-of-class instances filtering and model re-training in a transductive manner. In detail, given a pool of newly coming test data, S2OSC firstly filters the mostly distinct out-of-class instances using the pre-trained model, and annotates super-class for them. Then, S2OSC trains a holistic classification model by combing in-class and out-of-class labeled data with the remaining unlabeled test data in a semi-supervised paradigm. Furthermore, considering that data are usually in the streaming form in real applications, we extend S2OSC into an incremental update framework (I-S2OSC), and adopt a knowledge memory regularization to mitigate the catastrophic forgetting problem in incremental update. Despite the simplicity of proposed models, the experimental results show that S2OSC achieves state-of-the-art performance across a variety of OSC tasks, including 85.4% of F1 on CIFAR-10 with only 300 pseudo-labels. We also demonstrate how S2OSC can be expanded to incremental OSC setting effectively with streaming data.

Classification of Head, Neck, and Face Pains First Edition (WHS-MCH1): Position paper of the WHS Classification Committee

The WHS classification of Head, Neck and Face pain, Edition 1 Version 1 (WHS-MCH1) is the official document of the World Headache Society. It was conceptualized and developed by the Society’s Classification Committee. The work began with a clean slate to create a comprehensive, updated and holistic classification of headache disorders; where ‘headache’ was defined as any pain above the shoulders, thus including head, neck and face pain. This new classification reflects a scientifically robust understanding of disease and also places patient experience in the qualia of pain. It is a training manual to be used at the bedside and office as an aid to the diagnosis and management of headache disorders. The dynamic nature of this first ever live classification of headaches also means that ultra-rapid updates, or versions, will be available electronically. It is not a disease criteria but a classification criteria (1) and is useful to pick extended spectra and ‘mimickers’ of diseases. Although increased sensitivity usually comes at the expense of reduced specificity, an expanded spectrum of diseases in this case also means increased specificity. WHS-MCH1 is a syndromic classification. A syndrome is a recognizable complex of symptoms and physical findings which may have more than one aetiology. Although disease is nominalist and culture-relativistic (2), a syndrome based approach reflects the discipline of first widening the view of possibilities before analysing each to formulate a diagnostic hypothesis. Such an approach provides a useful framework for organizing the complexity of clinical experience in order to derive inferences about outcome and guide decisions about treatment. WHS-MCH1 has a vertical grouping designed for use by clinicians of all levels of experience; this is linked to the horizontal groupings which are syndrome-based. The syndrome groups are also interlinked to one another. This design enables clinicians to efficiently create the ‘big picture’ so as not to miss any diagnosis. Axis 1 and 2 are the vertical and horizontal grouping categories, respectively. Axis 3 is the patient narrative of bothersome symptoms and level of impairment. Axis 4 are biomarkers that may be derived from investigations and this is the best example of the continuum of better understanding of disease-defining markers. Axis 5 is an objective impairment scale that clinicians may choose based on availability. The World Headache Society hopes that the use of such a robust and inclusive framework will lead to better patient outcomes and improved patient and clinician satisfaction with the investigative and diagnostic process. Keywords: Classification; Syndromes; Headache disorders; Neck pain; Facial pain; Aaxis classification; Head pain; Face pain; Headache; Multiaxis

The Many Faces of Sagittal Synostosis: A Novel Classification and Approach to Diagnosis.

Purpose: Among the variable forms of craniosynostosis, sagittal synostosis represents the most common type of nonsyndromic single-suture fusion. There are many variations of this form that have been recognized in the literature. We aim to identify and categorize the clinical and radiological characteristics for patients with sagittal synostosis from 2014 to 2017. Secondly, to define a classification system wherein quantitative and qualitative descriptors are used to provide a better understanding of the disease process. Methodology: An institutional retrospective analysis identified all patients during the 2014 to 2017 period with computer tomography (CT) diagnosed single suture sagittal craniosynostosis (n = 51). Redo cases were excluded from the study. The data was collected from CT radiological evidence, clinical images, and medical notes. The patients were stratified into clinical assessment, radiological and neurological, and a statistical analysis was performed. Results: The average age for CT scans was 3 months and operative management 6.4 months, respectively. The most common presentation: Conclusions: Sagittal synostosis presented with many heterogeneous variations. The classic appearance of a sagittal ridge, occipital bullet and/or frontal bossing, with frontal interhemispheric and subarachnoid spaces, and complete sagittal fusion was most common. A holistic classification can aid in the assessment and prioritization of surgical intervention. Future longitudinal studies would provide additional insight into the relationships between clinical appearance and suture fusion.

Morphological classification and origin of lake depressions in Mongolia

An improved classification of the origin of lake depressions due to geological, geomorphological factors and climate change is a requirement of the day in Mongolia. We present a new holistic classification using comparative analysis method. This study suggests a two-tier classification in terms of origin and morphological feature of the lakes, which replaces the previous one-tier classification. Mongolia has identified 11 main and 26 subtypes of origin, and 8 main types of morphology, based on the features of 32 lake depressions. The result of the study shows that the lakes of Mongolia developed in 3 stages, first, affected by tectonic movement, followed by glacial and finally, owing to other exogenic factors. This morphological classification study will create the basic conditions for preserving and using these lakes more efficiently and ecologically in the future by making the classification of the origin of lake depression.

LCZ Generator: A Web Application to Create Local Climate Zone Maps

Since their introduction in 2012, Local Climate Zones (LCZs) emerged as a new standard for characterizing urban landscapes, providing a holistic classification approach that takes into account micro-scale land-cover and associated physical properties. In 2015, as part of the community-based World Urban Database and Access Portal Tools (WUDAPT) project, a protocol was developed that enables the mapping of cities into LCZs, using freely available data and software packages, yet performed on local computing facilities. The LCZ Generator described here further simplifies this process, providing an online platform that maps a city of interest into LCZs, solely expecting a valid training area file and some metadata as input. The web application (available athttps://lcz-generator.rub.de) integrates the state-of-the-art of LCZ mapping, and simultaneously provides an automated accuracy assessment, training data derivatives, and a novel approach to identify suspicious training areas. As this contribution explains all front- and back-end procedures, databases, and underlying datasets in detail, it serves as the primary “User Guide” for this web application. We anticipate this development will significantly ease the workflow of researchers and practitioners interested in using the LCZ framework for a variety of urban-induced human and environmental impacts. In addition, this development will ease the accessibility and dissemination of maps and their metadata.

Corporate real estate management and companies’ success: empirical evidence for a conceptual framework

PurposeThe purpose of this study is to explain the contribution of Corporate Real Estate Management (CREM) to corporate success and to substantiate it empirically. However, no empirically tested holistic concept classifies and explains the different success contributions of CREM in their mechanisms of action and organisational levels.Design/methodology/approachThis study develops a holistic two-dimensional model from existing literature to explain the relationship between CREM decisions and business success, and then tests it empirically using multidimensional data scaling from a telephone company survey (CATI) of 59 CREM managers sampled from the 200 largest German companies.FindingsThe created theoretical model holistically explains CREM success and existence as part of a non-property company, with specific performance drivers on specific organisational levels. The empirical data confirm that both dimensions of the model and, thus the measurement concept for modelling the CREM contribution to business success is robust across sectors and company/portfolio size in Germany.Originality/valueThe empirical confirmation of the conceptual model of CREM success provides novel support for the institutionalisation of the CREM function in companies and the holistic classification of different CREM research directions.

The classification of citing motivations: a meta-synthesis

Citation analysis has been a prevalent method in the field of information science, especially research on bibliometrics and evaluation, but its validity relies heavily on how the citations are treated. It is essential to study authors’ citing motivations to identify citations with different values and significance. This study applied a meta-synthesis approach to establish a new holistic classification of citation motivations based on previous studies. First, we used a four-step search strategy to identify related articles on authors’ citing motivations. Thirty-eight primary studies were included after the inclusion and exclusion criteria were applied and appraised using the Evidence-based Librarianship checklist. Next, we decoded and recoded the citing motivations found in the included studies, following the standard procedures of meta-synthesis. Thirty-five descriptive concepts of citation motivations emerged, which were then synthesized into 13 analytic themes. As a result, we proposed a comprehensive classification, including two main categories of citing reasons, i.e., “scientific motivations” and “tactical motivations.” Generally, the citations driven by scientific motivations serve as a rhetorical function, while tactical motivations are social or benefit-oriented and not easily captured through text-parsing. Our synthesis contributes to bibliometric and scientific evaluation theory. The synthesized classification also provides a comprehensive and unified annotation schema for citation classification and helps identify the useful mentions of a reference in a citing paper to optimize citation- based measurements.

Determination of Optimum Slope Design for Northern Boxcut in Zone 5 Khoemacau Copper Mine, Botswana

An optimum design of box cuts in soil formations is very crucial in order to obviate the major risk factors originating from the collapse of sidewalls and flooding of excavations during storm rainfall. The present paper aims to present a holistic classification of the Kalahari Formation stratigraphy in Zone 5 and define engineering properties of each lithological unit, in order to establish a safe working design. For the present objectives, collection of data was carried out through logging core from selected geotechnical boreholes drilled within vicinity of the proposed Northern mine box cut. Hydrogeological assessments and feasibility studies within the purview of study region were also considered. Geotechnical logging parameters gathered on site were derived from the Rock Mass Rating system (RMR) for design requirements [1]. Input parameters and material characteristics taken from laboratory test results provided by KCM were incorporated in the analysis. The box cut slopes were modelled in “Rocscience software” for evaluation of safety factor using “limit equilibrium method”. Slope optimization required the slope surface to be as steep as possible while maintaining an adequate factor of safety ranging from 1.5 - 1.8. For the box cut design with optimum safety, the recommended parameters are: stable slope angle—35° - 40°; ramp angle—8°, depth of pit—60 meters; bench width—4.9 meters and the bench length—13.25 meters.

Governing the sharing economy: Towards a comprehensive analytical framework of municipal governance

The sharing economy is having a transformative impact on our cities, and many municipalities are facing a challenge – how to systematically engage with the sharing economy to both mitigate its negative and enhance its positive impacts. Academic understanding of municipal governance mechanisms of the sharing economy remains poor. To address this gap, we develop a comprehensive analytical framework for municipal governance of the sharing economy, comprising five mechanisms (regulating, providing, enabling, self-governing, collaborating) and eleven roles. We employ a mixed-method approach comprising literature analysis, 139 semi-structured interviews, five workshops, three focus groups, and seven mobile research labs conducted in Amsterdam, Berlin, Gothenburg, London, Malmö, San Francisco and Toronto. We then go on to demonstrate how municipalities have positive and negative interactions with SEOs through various mechanisms. Explaining why municipalities differ in their governance approaches towards SEOs is an important area of future research. The framework contributes to knowledge on municipal governance by offering a holistic classification of mechanisms and roles of municipal governance relating to the sharing economy. In addition to its academic value, the framework has value for urban policy and planning, as it can help municipalities navigate the governance complexity and become more agile when engaging with SEOs.

地域高齢者を対象とした心理的・社会的・身体的側面からの類型化の試み

地域高齢者を対象とした心理的・社会的・身体的側面からの類型化の試み

THE REFLECTIONS OF PRE-SERVICE EFL TEACHERS ON OVERCOMING FOREIGN LANGUAGE TEACHING ANXIETY (FLTA) DURING TEACHING PRACTICUM

Whereas research on English as a Foreign Language Learning (EFLL) anxiety has been the focus of many researchers, English as a Foreign Language Teaching (EFLT) anxiety as a newly pop-up concept has gained lesser attention than EFLL. Moreover, the research on EFLL commonly focuses on the anxiety provoking factors. Yet again, there has been very limited research on how to overcome anxiety-provoking factors on both research areas. Accordingly, as this study was two of the three segments of a university-based project and the subject of anxiety-provoking factors was the issue of another study, the purpose of this study was firstly to explore how to overcome anxiety-provoking factors among pre-service EFL teachers while teaching English to young language learners at a real classroom setting as a part of teaching practicum. Secondly, this study also attempted to make a holistic classification rather than an atomistic one as it classified the findings of overcoming anxiety provoking factors into subtitles such as cognitive, affective, socio-cultural resolutions and some other official resolutions. In the study, 25 EFL pre-service teachers at Balikesir University were the sample group. The qualitative data was collected through background questionnaires, interviews and reflections. The study concluded that pre-service EFL teachers revealed their reflections on how to overcome anxiety-provoking factors as they experienced Foreign Language Teaching Anxiety (FLTA) whilst practicing teaching English to young language learners during teaching practicum courses. Some practical recommendations were given at the end of the study.

Multi-view dynamic facial action unit detection

We propose a novel convolutional neural network approach to address the fine-grained recognition problem of multi-view dynamic facial action unit detection. We leverage recent gains in large-scale object recognition by formulating the task of predicting the presence or absence of a specific action unit in a still image of a human face as holistic classification. We then explore the design space of our approach by considering both shared and independent representations for separate action units, and also different CNN architectures for combining color and motion information. We then move to the novel setup of the FERA 2017 Challenge, in which we propose a multi-view extension of our approach that operates by first predicting the viewpoint from which the video was taken, and then evaluating an ensemble of action unit detectors that were trained for that specific viewpoint. Our approach is holistic, efficient, and modular, since new action units can be easily included in the overall system. Our approach significantly outperforms the baseline of the FERA 2017 Challenge, with an absolute improvement of 14% on the F1-metric. Additionally, it compares favorably against the winner of the FERA 2017 Challenge.

Holistic versus feature-based binding in the medial temporal lobe

A central question for cognitive neuroscience is how feature-combinations that give rise to episodic/source memories are encoded in the brain. Although there is much evidence that the hippocampus (HIP) is involved in feature binding, and some evidence that other brain regions are as well, there is relatively little evidence about the nature of the resulting representations in different brain regions. We used multivoxel pattern analysis (MVPA) to investigate how feature combinations might be represented, contrasting two possibilities, feature-based versus holistic. Participants viewed stimuli that were composed of three source features – a person (face or body), a scene (indoor or outdoor), and an object (bike or luggage) – which were combined to make eight unique stimulus identities. We reasoned that regions that can classify the eight identities (a multiclass classification) but not the individual features (a binary classification) likely have a holistic representation of each identity. In contrast, regions that can classify the eight identities and can classify each feature are likely to contain feature-based representations of these identities. To further probe the extent of feature-based or holistic classification in each region, we developed and validated a novel approach that directly compares binary and multiclass classification. We found clear evidence for holistic representation in the parahippocampal cortex (PHC), consistent with theories that posit that pattern-separation-like binding mechanisms are not unique to the HIP. Further clarifying the mechanisms of feature binding should benefit from systematic comparisons of multi-feature representations and whether they vary with task, type of stimulus, and/or experience.