The Procedural Nature of Gothic Architecture and Microarchitecture

Insolvency prediction and credit rating are challenging tasks used to evaluate the creditworthiness of commercial enterprises based on qualitative and quantitative attributes. One way to approach these tasks is machine learning whereby prediction models are built from sample data. The advantage of machine learning is the automatization of the process obviating the need for human knowledge in most cases and thus, its high level of objectivity. Nevertheless, this approach does not claim to be perfect which is why it does not completely replace human knowledge. Since these models can be used as decision support for experts, interpretable models are desirable. Unfortunately, interpretable models are provided by only a few machine learners. Furthermore, some tasks in finance like credit rating often are multiclass problems. Multiclass classification is often achieved via meta-algorithms using multiple binary learners. However, most state-of-the-art meta-algorithms destroy the interpretability of binary models. In this thesis, we study the performance of interpretable models compared to non-interpretable models in insolvency prediction and credit rating. We look at disjunctive normal forms and decision trees of thresholds of financial ratios as interpretable models. We use random forests, artificial neural networks, and support vector machines as non-interpretable models. Furthermore, we use our own developed machine learning algorithm Thresholder to build disjunctive normal forms and interpretable multiclass models. For the task of insolvency prediction, we demonstrate that interpretable models are not inferior to non-interpretable black-box models. In a first case study, a real-life database with financial statements of 5152 enterprises is used to evaluate the performance for all models. In a second case study focused on credit rating, we show that interpretable multiclass models are even superior to non-interpretable multiclass models. We evaluate their performances on three real-life data sets divided into three rating classes. In these case studies, we compare different interpretable approaches concerning their model size and type of interpretability. We provide example models built on these real-life databases and an interpretation for them. The results show that interpretable threshold-based models are appropriate for classification problems in finance. For these tasks they are not inferior to more sophisticated models like support vector machines. Our algorithm Thresholder builds the smallest models while its performance is comparable to the other interpretable models. In our case study on credit rating, interpretable models perform better than for our case study on insolvency prediction. A possible explanation can be found in the nature of credit rating. In contrast to insolvencies, credit ratings are man-made. This implies that credit ratings are based on decisions by people thinking in interpretable rules, e.g., logical operations on thresholds. Thus, we assume that interpretable models match the problems and detect and represent these interpretable rules.

Objective of the study: The objective of this research was to analyze how the processes of creation and appropriation of value are manifested in a horizontal agribusiness networkMethodology / approach: A case study was carried out in the Cerrado Mineiro Region (CMR) in which documentary research and interviews were used as source, whose data were analyzed through content analysis by frequency. Originality / Relevance: There is little evidence in the literature about studies that explore processes of joint value creation and appropriation in horizontal networks. In view of this gap, the study proceeds by individually addressing the processes of creation and appropriation of value considering the nature of cooperation of the networks.Main results: In relation to the results achieved, CMR creates value in cost reduction, differentiation by product, market expansion, expertise in the production of coffee, differentiation, dissemination and strengthening of the brand. Regarding the appropriation of value, CMR appropriates itself for the commercialization of potential quality coffee, the learning process, the premium paid in the cooperative's coffee, the valorization of the producer and for having a unique way for the production of coffee.Theoretical / methodological contributions: Among the main contributions of this study, the individual analysis highlights the processes of creation and appropriation of value applied to horizontal networks. The considerations reinforce the fine line between the processes of creation and appropriation of relational value. That is, in addition to developing internal competencies and essential skills for the creation and appropriation of value, organizations must devise strategies to achieve a balance between these processes.

Models as interpreters (with a case study from pain science)

Service Oriented Architecture (SOA) and Business Rules (BR) are used by organization to adapt to changes. The disadvantage of this approach is that processes need to be defined in advance often requiring labor-intensive and time-consuming modeling processes. Usually, only a limited number of variations in processes are supported which have to be defined in design time. Hence, a shift from static to dynamic business processes creation is required. Semantic Web Services (SWS) can be used as a technology to create dynamic processes. Although there has been extensive research in the field of SWS and BR, there is scant research on the combination of them. In this paper, we propose an architecture based on BR and SWS to create dynamic business processes. Using a pre-agreed domain ontology to ensure the compatibility between SWSs and BRs, a new process is created by selecting the decision services using BRs and composing decision services with necessary assistant services. An illustrative study is used to demonstrate and evaluate the architecture on its feasibility in dynamic process creation. In this way, SOA systems in government environment can create a process with their services dynamically without having to hardcode the process. This can help the organization to efficiently adapt to changes in policies and legislation.

Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys

The luminosity of X-ray pulsars powered by accretion on to magnetized neutron stars covers a wide range over a few orders of magnitude. The brightest X-ray pulsars recently discovered as pulsating ultraluminous X-ray sources reach accretion luminosity above $10^{40}\, {\rm erg\ \rm s^{-1}}$ which exceeds the Eddington value more than by a factor of 10. Most of the energy is released within small regions in the vicinity of magnetic poles of accreting neutron star - in accretion columns. Because of the extreme energy release within small volume accretion columns of bright X-ray pulsars are one of the hottest places in the Universe, where the internal temperature can exceed 100 keV. Under these conditions, the processes of creation and annihilation of electron-positron pairs can be influential but have been largely neglected in theoretical models of accretion columns. In this Letter, we investigate properties of a gas of electron--positron pairs under physical conditions typical for accretion columns. We argue that the process of pair creation can crucially influence both the dynamics of the accretion process and internal structure of accretion column limiting its internal temperature, dropping the local Eddington flux and increasing the gas pressure.

This chapter consists of two main parts. After an introduction, the first part briefly considers the way that historical processes have been represented in ABM to date. This makes it possible to draw more general conclusions about the limitations of ABM in dealing with distinctively historical (as opposed to merely dynamic) processes. The second part of the chapter presents a very simple ABM in which three such distinctively historical processes are analysed. These are the possible significance of unique individuals—the so-called Great Men, the invention and spread of social innovations from specific points in time and the creation of persistent social structures (also from specific points in time). The object of the chapter is to advance the potential applicability of ABM to historical events as understood by historians (rather than anthropologists or practitioners of ABM.)

The thesis revisits Gartner, W. B. (1985) position on entrepreneurship and small business research, namely, that the creation process and the organising process of the small business needs to be separately explored to learn more about the small business population. It is not clear if the creation process of the small business can be separated from the organising process. There is no definition available to describe the creation process and it is difficult to observe the phenomenon, if possible at all. Probably an adequate method to learn more about the creation process is through the application of retrospective interviews. Taking account of the difficulties to conceptualise such research, case study method is proved to be the most appropriate research method to investigate the creation process of the small business following Yin, K. R. (2003) linear analytic structure. Such choice underlines the debate if the separation of creating and operating of the small business could be a feasible approach. Within the theoretical framework the case study focuses on four concepts: Embeddedness; Social Capital; Reciprocity; and Communication. The research applies booth secondary quantitative data analysis and qualitative data analysis. The quantitative data source is Eurostat data basis. The qualitative data source is a selection of focused interviews with owner-managers of small businesses registered in Hungary. The research tests five hypothesis. One hypothesis testing requires secondary quantitative data analysis, the other four hypothesis require qualitative data analysis. The interview transcripts are analysed by an online content analysis software. Besides content analysis, the participants’ interpretations are organized into categories supported by interview quotations. The structure of the thesis follows general guidelines: introduction opens theory elaboration applying multidisciplinary approach. Part I is the research design. Part II is the literature review and the data analysis. Part III is the case study report which includes an outline of practical and theoretical aspects for future research directions, too. Part IV lists the appendices of the case study and the reference.

This exploratory and practical case study examines one country’s white papers on disability policies whilst drawing upon experiences of an international technical cooperation project. The creation process, contents, and functions of white papers on disability in Mongolia are analysed descriptively using document data and the author’s field notes. This case study on the practice which can be considered macro social work reveals that the creation of the Mongolian white papers was a constructive and dynamic process, participated in by both domestic and international actors. It was found that white papers have multiple functions, such as sharing fundamental information to promote the participation of disabled people and to monitor disability-inclusive policies. This article evinces how white papers on disability can be a foundation of knowledge and information on disability-inclusive policies and development for various stakeholders.

In developing tourist attractions, government planning authorities and entrepreneurs face a challenging task trying to cater to the interests of foreign visitors while meeting the needs of the local community. This paper presents the case of tourism development as a dynamic process in which the “tourist–local divide” is negotiated and the welfare of both groups monitored. This argument is empirically developed with the aid of two case studies on heritage tourism in Singapore: the adaptive re–use of old shophouses after their conversion into boutique hotels, and the re–invention of street activities as tourist sites. The commoditisation thesis advanced by many tourism writers is critiqued. I argue that heritage development is geared towards Singaporeans as much as it is towards tourists, and the effects of commoditisation are not always negative for the host community. Rather than a static object, heritage is an ever–changing product influenced by the combined effects of economic development, tourism and socio–cultural forces at the local scale. For this reason, the notion that commoditisation leads to “inauthenticity” is re–evaluated and a more optimistic prognosis on heritage tourism is offered.

Food security is crucial for national stability and public welfare. Since the 21st century, China’s grain production has been significantly influenced by the rapid process of urbanization. In this context, this paper systematically measures the multidimensional coupling patterns and dynamic coupling processes between urbanization and grain production from 2000 to 2022, and preliminarily summarizes the complex coupling mechanisms within the Chinese context. The goal is to provide scientific references for achieving high-quality coordinated development of urbanization and grain production in China. The study reveals the following key findings: (1) The coupling relationship between urbanization and grain production exhibits both regional heterogeneity and temporal variability, demonstrating specific levels of coupling and dynamic processes under distinct spatiotemporal conditions. (2) Between 2000 and 2022, both urbanization and grain production patterns in China underwent significant reconstruction, with the coupling coordination level displaying a long-term spatial pattern of “high in the north, low in the south; high in the east, low in the west.” Although there is an overall upward trend in coupling coordination states, spatial imbalances and dimensional heterogeneity persist. (3) Since the beginning of the 21st century, the dynamic coupling processes between provincial urbanization and grain production have primarily manifested as two types: simultaneous increase (with urbanization outpacing grain production) and urban increase accompanied by grain production decrease. Various dynamic coupling types exhibit significant spatial clustering, and the multidimensional dynamic coupling processes reveal notable similarities. (4) The evolution of coupling states demonstrates an overall trend of optimization, with clear bidirectional migration trends observed in coupling dynamics, primarily transitioning from simultaneous increase (urbanization outpacing grain production) to urban increase with grain production decrease, and vice versa. (5) The formation of the complex coupling relationship between urbanization and grain production in the Chinese context is fundamentally influenced by changes in population quantity and structure between urban and rural areas, shifts in land use, economic transformation, regional specialization, technological interactions, and factor mobility. These influences exhibit significant negative effects in the domains of population, land, and economy, while showcasing notable positive effects in terms of technology and factor mobility.

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future.

As artificial intelligence continues to reshape the healthcare industry, a growing concern among professionals and patients is the "black-box" nature of many machine learning models. While accuracy remains important, trust in AI decisions is equally vital, especially in critical areas like diagnosis and treatment planning. This paper explores the role of Explainable Artificial Intelligence (XAI) in building that trust by making machine learning outputs more transparent and understandable. Using real-world datasets and a case study in cardiovascular disease prediction, we evaluate how interpretable models and explanation techniques like SHAP and LIME improve clinician acceptance and decision-making. A structured questionnaire reveals insights from healthcare professionals on their comfort and reliance on AI tools. This study contributes to the understanding that for AI to be truly effective in healthcare, it must not only be smart—but also explain itself.

Translanguaging and trans-semiotizing research has problematized the static view of language and argued that meaning making is a dynamic, material, social, and historical process across multiple timescales in complex eco-social systems. The second author proposed the concept of trans-semiotizing as an alternative lens to study language teaching and learning. In this autoethnographic study, the dynamic processes of online language learning and teaching are examined by analysing the semiotic resources, trans-semiotic practices, and the coordination of different semiotic resources. To capture such dynamic processes and the semiotic resources involved, the first author setup multiple cameras and used screen recording to document my teaching. Data include recordings of my computer screens, video recordings of my physical environment, facial expressions, body movements, screen shots of my social media posts, and my teaching notes. We draw on Lemke’s dynamic eco-social system concept to discuss how semiotic resources are used in online language teaching and learning across different timescales.

Purpose– This paper aims to examine the dynamic process of knowledge creation of the international new venture (INV) through the interaction with network partners. The process of how INVs make use of external sources for the acquisition of international market knowledge is not well-understood.Design/methodology/approach– To uncover the dynamics of the knowledge creation process, the authors applied event-driven process research by following the internationalization process of four INVs in real time. More specifically, they adopted qualitative diary research combined with periodic follow-up interviews as the main data collection method. A visual mapping strategy was used for the analysis of the process data.Findings– The analysis shows that different pathways of knowledge acquisition through congenital learning, searching, vicarious learning and grafting interact with each other. Grafting and experiential learning alongside the partner lead to the acquisition of internationalization knowledge in particular. Knowledge sources for international market knowledge are proactively created by the entrepreneurs. The wider effectual stakeholder network constitutes an important source for international market knowledge.Research limitations/implications– The authors followed the early internationalization process of the case firm in real time over a 10-month period. This provides a limited window of observation. Future research might extend the observation period to examine further the evolutionary nature of the different learning types throughout the growth of the INV. The case firms operate in Internet-enabled businesses and are all located in the same country and city (i.e. Colombia and the city of Medellin). Future studies might focus on firms operating in different industries and geographical areas.Practical implications– Congenital technological knowledge is a prerequisite for internationalization. The entrepreneur, however, does not need to rely on congenital international market knowledge. Such knowledge can be developed through network partners. Foreign business and institutional knowledge can be obtained vicariously, also from professional advisors. Internationalization knowledge, however, needs to be developed in close interaction with an international cooperation partner, where a strong relationship commitment prevails.Originality/value– The authors use effectuation theory combined with process research methods to gain insights into the dynamics of knowledge creation within the INV. Thereby, they are able to shed light on the dynamics of the process that is difficult to capture through cross-sectional research designs. Research on the internationalization process of young ventures in the context of Latin America is scarce. Therefore, the paper contributes new knowledge about the development of these firms in that particular region.