Formal Definition Research Articles

Quantum state tomography on closed timelike curves using weak measurements

Abstract Any given prescription of quantum time travel necessarily endows a Hilbert space to the chronology-violating (CV) system on the closed timelike curve (CTC). However, under the two foremost models, Deutsch’s prescription (D-CTCs) and postselected teleportation (P-CTCs), the CV system is treated very differently: D-CTCs assign a definite form to the state on this system, while P-CTCs do not. To further explore this distinction, we present a methodology by which an operational notion of state may be assigned to their respective CV systems. This is accomplished via a conjunction of state tomography and weak measurements, with the latter being essential in leaving any notions of self-consistency intact. With this technique, we are able to verify the predictions of D-CTCs and, perhaps more significantly, operationally assign a state to the system on the P-CTC. We show that, for any given combination of chronology-respecting input and unitary interaction, it is always possible to recover the unique state on the P-CTC, and we provide a few specific examples in the context of select archetypal temporal paradoxes. We also demonstrate how this state may be derived from analysis of the P-CTC prescription itself, and we explore how it compares to its counterpart in the CV state predicted by D-CTCs.

Septic Cardiomyopathy: Difficult Definition, Challenging Diagnosis, Unclear Treatment

Sepsis is a systemic inflammatory response syndrome of suspected or confirmed infectious origin, which frequently culminates in multiorgan failure, including cardiac involvement. Septic cardiomyopathy (SCM) remains a poorly defined clinical entity, lacking a formal or consensus definition and representing a significant knowledge gap in critical care medicine. It is an often-underdiagnosed complication of sepsis. The only widely accepted aspect of its definition is that SCM is a transient myocardial dysfunction occurring in patients with sepsis, which cannot be attributed to ischemia or pre-existing cardiac disease. The pathogenesis of SCM appears to be multifactorial, involving inflammatory cytokines, overproduction of nitric oxide, mitochondrial dysfunction, calcium homeostasis dysregulation, autonomic imbalance, and myocardial edema. Diagnosis primarily relies on echocardiography, with advanced tools such as tissue Doppler imaging (TDI) and global longitudinal strain (GLS) providing greater sensitivity for detecting subclinical dysfunction and guiding therapeutic decisions. Traditional echocardiographic findings, such as left ventricular ejection fraction measured by 2D echocardiography, often reflect systemic vasoplegia rather than intrinsic myocardial dysfunction, complicating accurate diagnosis. Right ventricular (RV) dysfunction, identified as a critical component of SCM in many studies, has multifactorial pathophysiology. Factors including septic cardiomyopathy itself, mechanical ventilation, hypoxemia, and hypercapnia—particularly in cases complicated by acute respiratory distress syndrome (ARDS)—increase RV afterload and exacerbate RV dysfunction. The prognostic value of cardiac biomarkers, such as troponins and natriuretic peptides, remains uncertain, as these markers primarily reflect illness severity rather than being specific to SCM. Treatment focuses on the early recognition of sepsis, hemodynamic optimization, and etiological interventions, as no targeted therapies currently exist. Emerging therapies, such as levosimendan and VA-ECMO, show potential in severe SCM cases, though further validation is needed. The lack of standardized diagnostic criteria, combined with the heterogeneity of sepsis presentations, poses significant challenges to the effective management of SCM. Future research should focus on developing cluster-based classification systems for septic shock patients by integrating biomarkers, echocardiographic findings, and clinical parameters. These advancements could clarify the underlying pathophysiology and enable tailored therapeutic strategies to improve outcomes for SCM patients.

A Framework for Investigating Discording Communities on Social Platforms

In recent years, polarization on social media has risen significantly. Social platforms often feature a range of topics that give rise to communities of users with diametrically opposed views, who tend to avoid engaging with others having different perspectives. We call these types of communities “diverging communities”. Examples include communities of supporters and skeptics of climate change or COVID-19 vaccines. In this paper, we aim to investigate this phenomenon. To do so, we first propose a formal definition of discording communities. We then present a framework for investigating the behavior of users of discording communities on a social platform. Our framework is general in that it can be adapted to any social platform where users discuss a topic that polarizes them into communities with diametrically opposed viewpoints rejecting confrontation. Our framework considers not only the structure of communities but also the content of the messages posted by their users. Finally, it can also handle the temporal evolution of the polarization level of both communities and their users. In addition to proposing a formal definition of diverging communities and presenting our framework, we illustrate the results of an extensive experimental campaign carried out on two case studies involving Reddit and X and show how our framework is able to identify a number of features that distinguish the users of one diverging community from the users of the other.

Empowering healthcare: the link of self-efficacy, employee engagement, and citizenship behaviors

Purpose This study investigates employee engagement within private healthcare settings, with a particular emphasis on the connection between occupational self-efficacy and organizational citizenship behaviors. Design/methodology/approach A mixed-methods approach was utilized for this research. Interviews were conducted with employees across different managerial levels to identify various employee engagement strategies implemented in eight private hospitals in West Bengal. Subsequently, a survey of 414 healthcare employees from these institutions was conducted using a structured questionnaire. The study employed Confirmatory Factor Analysis, linear regression, and mediation analysis using the Sobel test to explore the relationships between variables. Findings The study revealed a significant positive correlation among all variables. Employee engagement was identified as a mediating factor between occupational self-efficacy and organizational citizenship behavior. Research limitations/implications This research is focused exclusively on the healthcare sector, with no data collected from other sectors, which may limit the generalizability of the findings. The sample size and geographic scope were restricted, which may not capture the full diversity of perspectives within the healthcare sector. Practical implications This research indicates that OSE promotes OCB, with this effect amplified when mediated by EE. Thus, fostering EE can significantly contribute to achieving OCB, a crucial factor in the healthcare sector. Given these insights, public healthcare institutions, which often serve a larger and more diverse patient population, could benefit from adopting similar engagement-enhancing practices. In these high-pressure environments, where employees frequently need to exceed their formal role definitions, cultivating a culture of engagement becomes paramount. By implementing targeted EE initiatives, government hospitals can potentially improve OCB among their staff, leading to enhanced patient care and operational efficiency. Government healthcare HR professionals can enhance OSE through training, coaching, and talent recognition programs. Empowering employees via skill development and participative decision-making can boost OSE and its positive impact on OCB. Originality/value This study offers novel insights that can inform practices in both private and public healthcare sectors, representing a pioneering effort in the Indian context. By examining these dynamics, the research aims to contribute to the development of strategies that enhance employee engagement, ultimately leading to improved patient care that goes beyond standard job descriptions.

Scientific Definitions and Measurements of Second-Growth, Mature, and Old-Growth Forests

Abstract We examined US scientists’ (1) use of second-growth, mature, and old-growth forest in scientific publication titles, (2) definitions of second-growth, mature, and old-growth forest, and (3) sampling methods in second-growth, mature, and old-growth forest. “Second-growth forest” (55 titles) and “mature forest” (40) experienced the most frequent use in the 2010s. “Old-growth forest” (247) had the most use in the 1990s. Definitions of second-growth forest originated from successional theory and were consistent across scientists. Definitions of mature forest lacked consensus. Definitions of old-growth forest increased in complexity as scientists integrated biological, social, and political factors. Soil was the most frequent abiotic factor measured in mature and old-growth forests. In second-growth forests it was dead organic matter. Trees were the most common life form measured in all forests. The results show that researchers would benefit from a clearer differentiation between second-growth and mature forest and a formal definition for mature forest.

Leadership development programs in healthcare organizations: What they are and how to assemble them.

In the highly competitive, perennially-changing healthcare industry, management prowess is vitally needed by those in positions of authority, but in isolation it is insufficient for delivering optimal results. Instead, management proficiencies must be complemented by leadership skills which amplify managerial efforts and outcomes. One avenue for building leadership acumen in healthcare organizations involves the establishment of leadership development programs. These programs generally assemble a range of opportunities permitting managers to learn about leadership and acquire associated proficiencies. Leadership development programs afford many benefits for healthcare providers, but in order to capitalize fully, thoughtful assembly is required. To shed light on this important topic, this article provides an overview of leadership development programs, noting a formal definition, stipulating associated benefits, and discussing options for assembly, all through the lens of Willis Knighton Health's leadership development initiatives and experiences.

Ontologies in modelling and analysing of big genetic data

To systematize and effectively use the huge volume of experimental data accumulated in the field of bioinformatics and biomedicine, new approaches based on ontologies are needed, including automated methods for semantic integration of heterogeneous experimental data, methods for creating large knowledge bases and self-interpreting methods for analyzing large heterogeneous data based on deep learning. The article briefly presents the features of the subject area (bioinformatics, systems biology, biomedicine), formal definitions of the concept of ontology and knowledge graphs, as well as examples of using ontologies for semantic integration of heterogeneous data and creating large knowledge bases, as well as interpreting the results of deep learning on big data. As an example of a successful project, the Gene Ontology knowledge base is described, which not only includes terminological knowledge and gene ontology annotations (GOA), but also causal influence models (GO-CAM). This makes it useful not only for genomic biology, but also for systems biology, as well as for interpreting large-scale experimental data. An approach to building large ontologies using design patterns is discussed, using the ontology of biological attributes (OBA) as an example. Here, most of the classification is automatically computed based on previously created reference ontologies using automated inference, except for a small number of high-level concepts. One of the main problems of deep learning is the lack of interpretability, since neural networks often function as “black boxes” unable to explain their decisions. This paper describes approaches to creating methods for interpreting deep learning models and presents two examples of self-explanatory ontology-based deep learning models: (1) Deep GONet, which integrates Gene Ontology into a hierarchical neural network architecture, where each neuron represents a biological function. Experiments on cancer diagnostic datasets show that Deep GONet is easily interpretable and has high performance in distinguishing cancerous and non-cancerous samples. (2) ONN4MST, which uses biome ontologies to trace microbial sources of samples whose niches were previously poorly studied or unknown, detecting microbial contaminants. ONN4MST can distinguish samples from ontologically similar biomes, thus offering a quantitative way to characterize the evolution of the human gut microbial community. Both examples demonstrate high performance and interpretability, making them valuable tools for analyzing and interpreting big data in biology.

KBX: Verified Model Synchronization via Formal Bidirectional Transformation

Complex safety-critical systems require multiple models for a comprehensive description, resulting in error-prone development and laborious verification. Bidirectional transformation (BX) is an approach to automatically synchronizing these models. However, existing BX frameworks lack formal verification to enforce these models’ consistency rigorously. This paper introduces KBX, a formal bidirectional transformation framework for verified model synchronization. First, we present a matching logic-based BX model, providing a logical foundation for constructing BX definitions within the \(\mathbb{K}\) framework. Second, we propose algorithms to synthesize formal BX definitions from unidirectional ones, which allows developers to focus on crafting the unidirectional definitions while disregarding the reverse direction and missing information recovery for synchronization. Afterward, we harness \(\mathbb{K}\) to generate a formal synchronizer from the synthesized definitions for consistency maintenance and verification. To evaluate the effectiveness of KBX, we conduct a comparative analysis against existing BX frameworks. Furthermore, we demonstrate the application of KBX in constructing a BX between UML and HCSP for real-world scenarios, showcasing an 72% reduction in BX development effort compared to manual specification writing in \(\mathbb{K}\) .

A Survey of Heuristics for Profile and Wavefront Reductions

This article surveys heuristic methods for profile and wavefront reductions. These graph layout problems represent a challenge for optimization methods and heuristics especially. The article presents the graph layout problems with their formal definition. It provides an ample perspective of techniques for designing heuristic methods for these graph layout problems but concentrates on the approaches and methodologies that yield high-quality solutions. Thus, this work references the most relevant studies in the associated literature and discusses the current state-of-the-art heuristics for these graph layout problems.

Low-Code and No-Code in Enterprise Development: Bridging the Gap Between IT and Business

Low-code development has emerged as a transformative concept in both academia and the software industry, often discussed alongside model-driven engineering and domain-specific languages. Usability plays a critical role in low- code contexts, given that many users lack programming expertise. While grey literature frequently praises the high usability of low-code tools, this paper systematically reviews academic and industry research to understand the relationship between low-code development and usability. Using a formal search protocol, 38 relevant studies were analyzed to identify key usability factors and how users perceive these tools. Despite growing interest, no formal definition of low-code has been universally adopted. This paper proposes a definition, highlights usability's role, and identifies further research avenues to strengthen the field of low-code development. Keywords Low-code, no-code, citizen development, IT democratization, business-IT collaboration, digital transformation, agile development, machine learning, usability, metadata-driven applications

A Randomized Response Framework to Achieve Differential Privacy in Medical Data

In recent years, differential privacy has gained substantial traction in the medical domain, where the need to balance privacy preservation with data utility is paramount. As medical data increasingly relies on cloud platforms and distributed sharing among multiple stakeholders, such as healthcare providers, researchers, and policymakers, the importance of privacy-preserving techniques has become more pronounced. Trends in the field focus on designing efficient algorithms tailored to high-dimensional medical datasets, incorporating privacy guarantees into federated learning for distributed medical devices, and addressing challenges posed by adversarial attacks. Our work lays a foundation for these emerging applications by emphasizing the role of randomized response within the broader differential privacy framework, paving the way for advancements in secure medical data sharing and analysis. In this paper, we analyze the classical concept of a randomized response and investigate how it relates to the fundamental concept of differential privacy. Our approach is both mathematical and algorithmic in nature, and our purpose is twofold. On the one hand, we provide a formal and precise definition of differential privacy within a natural and convenient probabilistic—statistical framework. On the other hand, we position a randomized response as a special yet significant instance of differential privacy, demonstrating its utility in preserving individual privacy in sensitive data scenarios. To substantiate our findings, we include key theoretical proofs and provide indicative simulations, accompanied by open-access code to facilitate reproducibility and further exploration.

The Law of Ukraine “On Public Consultations” and its impact on the evolution of the right to participate in the management of public affairs: theoretical and practical analysis

The opportunity for private individuals to engage in addressing issues of national and local significance is exercised through participation in the management of public affairs. This participation, a constitutional right enshrined in the Constitution of Ukraine, now holds a broader scope and greater impact than initially anticipated when the Basic Law was adopted at the end of the 20th century. Ongoing changes within Ukrainian society and the rapid evolution of administrative law have given rise to new legal avenues for individual engagement and interaction with public authorities. Effective regulation of public relations between government entities, individuals, and civil society organizations is a vital characteristic of a rule-of-law state. Currently, the effectiveness of citizens’ exercise of this constitutional right is measured not only by their ability to vote in elections, participate in referendums, or hold public office but also by their capacity to contribute to decision-making by public authorities. One such legal instrument facilitating this participation is public consultation, which is becoming a crucial component in fostering collaboration between government and civil society on matters of societal and state development. The recently enacted Law of Ukraine “On Public Consultations” introduces, for the first time, a formal definition of public consultation at the regulatory level. It establishes principles for their implementation, identifies participants eligible for involvement, sets forth procedural guidelines, and outlines mechanisms for reporting and monitoring outcomes. These provisions ensure that public consultation serves as an effective tool for involving the public in policy development and for facilitating participation in the formulation of regulatory and legislative acts, rather than merely functioning as a formal process. The enactment of this law, which provides an additional mechanism for public involvement, aligns with a broader European trend toward expanding public participation in decision-making processes at both local and central levels. Nonetheless, this legislation also contains several shortcomings that may hinder the practical implementation of some of its provisions and limit the extent of civil society’s influence on governmental decision-making. The aim of this article is to examine the Law of Ukraine “On Public Consultations” and assess its implications for the evolving relationship between the State and civil society.

Foundations of Data Availability Sampling

Towards building more scalable blockchains, an approach known as data availability sampling (DAS) has emerged over the past few years. Even large blockchains like Ethereum are planning to eventually deploy DAS to improve their scalability. In a nutshell, DAS allows the participants of a network to ensure the full availability of some data without any one participant downloading it entirely. Despite the significant practical interest that DAS has received, there are currently no formal definitions for this primitive, no security notions, and no security proofs for any candidate constructions. For a cryptographic primitive that may end up being widely deployed in large real-world systems, this is a rather unsatisfactory state of affairs. In this work, we initiate a cryptographic study of data availability sampling. To this end, we define data availability sampling precisely as a clean cryptographic primitive. Then, we show how data availability sampling relates to erasure codes. We do so by defining a new type of commitment schemes which naturally generalizes vector commitments and polynomial commitments. Using our framework, we analyze existing constructions and prove them secure. In addition, we give new constructions which are based on weaker assumptions, computationally more efficient, and do not rely on a trusted setup, at the cost of slightly larger communication complexity. Finally, we evaluate the trade-offs of the different constructions.

Goal-based performance metrics for ice hockey accounting for goal importance

The evaluation of player performance is an important topic in sports analytics and is used for team management, scouting, and in sports broadcasts. When evaluating the performance of ice hockey players, many metrics are used, including traditional metrics such as goals, assists, and points and more recent metrics such as Corsi and expected goals. One weakness of such metrics is that they do not consider the context in which the value for the metric was assigned. Other advanced metrics have been introduced, but as they are not easily explainable to practitioners, they may not make it into the hockey discourse. In this paper we introduce new goal-based metrics that (i) are based on traditional, well-known metrics, and thus easily understandable, (ii) take context into account in the form of time, manpower differential, and goal differential and (iii) add a new aspect by taking into account the importance of goals regarding their contribution to team wins and ties. We describe the intuitions behind the metrics, give formal definitions, evaluate the metrics and show correlations to the traditional metrics. We have used data from seven NHL seasons and show which players stand out with respect to the number of goals and the importance of goals.

SimpleSets: Capturing Categorical Point Patterns with Simple Shapes.

Points of interest on a map such as restaurants, hotels, or subway stations, give rise to categorical point data: data that have a fixed location and one or more categorical attributes. Consequently, recent years have seen various set visualization approaches that visually connect points of the same category to support users in understanding the spatial distribution of categories. Existing methods use complex and often highly irregular shapes to connect points of the same category, leading to high cognitive load for the user. In this paper we introduce SimpleSets, which uses simple shapes to enclose categorical point patterns, thereby providing a clean overview of the data distribution. SimpleSets is designed to visualize sets of points with a single categorical attribute; as a result, the point patterns enclosed by SimpleSets form a partition of the data. We give formal definitions of point patterns that correspond to simple shapes and describe an algorithm that partitions categorical points into few such patterns. Our second contribution is a rendering algorithm that transforms a given partition into a clean set of shapes resulting in an aesthetically pleasing set visualization. Our algorithm pays particular attention to resolving intersections between nearby shapes in a consistent manner. We compare SimpleSets to the state-of-the-art set visualizations using standard datasets from the literature.

Classification of direct threats to the conservation of ecosystems and species 4.0.

Identifying and assessing the magnitude of direct threats to ecosystems and species are critical steps to prioritizing, planning, implementing, and assessing conservation actions. Just as medical clinicians and researchers need a standard way to talk about human diseases, conservation practitioners and scientists need a common and comprehensive language to talk about the threats they are facing to facilitate joint action, evaluation, and learning. To meet this need, in 2008 the IUCN Species Survival Commission and the Conservation Measures Partnership produced the first version of a common threats classification with the understanding that it would be periodically updated to take into account new information and learning. We present version 4.0 of this classification. For this latest update, we reviewed existing versions and derivatives of the original classification, over 1000 citations of the classification, threats data from over 2900 real-world conservation projects, and comments from many users. Based on our findings, we made changes to the threats classification scheme, including addition of a level 0 threat class, refinement of levels 1 and 2 threat categories, and addition of the threat "Fencing & walls" to level 2. Also added were level 3 threat types and modifiers that provide a more detailed description of different types of direct threats and thus allow users to fine-tune analyses and actions. The update also clarifies how to treat various stressors, including natural disaster events and climate change. As a result of these changes, we revised the formal definition of direct threats. They include human actions that are the direct cause of ecosystem or species-population degradation and loss, such as agriculture, transport, natural resource use, and ecosystem management. They also include ultimate stressors in natural systems whose dynamics have been altered by the effects of current or historical human actions, such as invasive or problematic native species, pollution, natural disasters, and climate change.

Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets.

Molecular profiling of different omic-modalities (e.g., DNA methylomics, transcriptomics, proteomics) in biological systems represents the basis for research and clinical decision-making. Measurement-specific biases, so-called batch effects, often hinder the integration of independently acquired datasets, and missing values further hamper the applicability of typical data processing algorithms. In addition to careful experimental design, well-defined standards in data acquisition and data exchange, the alleviation of these phenomena particularly requires a dedicated data integration and preprocessing pipeline. This review aims to give a comprehensive overview of computational methods for data integration and missing value imputation for omic data analyses. We provide formal definitions for missing value mechanisms and propose a novel statistical taxonomy for batch effects, especially in the presence of missing data. Based on an automated document search and systematic literature review, we describe 32 distinct data integration methods from five main methodological categories, as well as 37 algorithms for missing value imputation from five separate categories. Additionally, this review highlights multiple quantitative evaluation methods to aid researchers in selecting a suitable set of methods for their work. Finally, this work provides an integrated discussion of the relevance of batch effects and missing values in omics with corresponding method recommendations. We then propose a comprehensive three-step workflow from the study conception to final data analysis and deduce perspectives for future research. Eventually, we present a comprehensive flow chart as well as exemplary decision trees to aid practitioners in the selection of specific approaches for imputation and data integration in their studies.

Magical Realism: The “Problem of Definition”

Whilst the theoretical study of magical realist literature is broad and wide-ranging, there is a remarkable lack of consensus among critics on how the mode should be defined, how its techniques should be described, and how it corresponds with both the contexts of its production and its contemporary neighboring genres. This essay maps some of the dominant avenues that have been explored in the field, illuminating where the disagreements lie and highlighting the sheer profusion of critical approaches that have been competing for attention since magical realism first began to receive academic attention with the publication of Gabriel García Márquez’s One Hundred Years of Solitude in 1967, which have inevitably contributed to the confusion about how works of magical realism should be identified and interpreted. Ultimately, it is my contention that the study of magical realism is limited by the critical convention that considers the mode to have begun in Latin America in the 1950s, and often treats García Márquez as its originator: texts emerging from outside of this context are excluded from critical discussion, even where they comply with all formal definitions of the mode. My essay interrogates this convention, which I view as producing an untenable internal contradiction that I identify as a key contributing factor in the so-called “problem of definition” with which the field has long been plagued, and proposes that historical or geographical particulars cannot feature in a working definition of magical realism.

Unveiling New Dimensions in Soft Disemigraph Theory: Lexicographic and Restricted Lexicographic Products

Soft set theory provides a systematic approach for handling imprecision and uncertainty by categorizing elements of a set based on specific parameters. In semigraph theory, soft semigraphs utilize this approach, offering a parameterized perspective that has significantly advanced the field through effective parameter management. Building on this foundation, disemigraphs extend semigraphs by incorporating directional relationships among vertices, making them ideal for modeling scenarios where the sequence and direction of connections are crucial. In this paper, we introduce and explore the lexicographic product, and restricted lexicographic product of soft disemigraphs. We provide formal definitions, illustrative examples, and detailed proofs of several theorems to investigate the properties of these product operations.

A formal approach to spatio-temporal modeling of game systems

This article introduces FAST-GM (Formal Approach to Spatio-Temporal Game Modeling), a new unified approach to formal modeling of game entities and their interactions that integrates temporal and probabilistic dimensions, thus offering a comprehensive framework for capturing the dynamics of game systems. Built upon the principles of extended temporal logic and probability theory, FAST-GM accurately describes complex game mechanics and how they evolve. A formal definition of game entities was considered. Their states and interactions were explored. The methods for integrating temporal and probabilistic elements into gameplay were discussed. The applicability of FAST-GM for game balancing, formal verification of game scenarios, and automated generation of test cases was analyzed. Its scalability and adaptability in various game genres were assessed. The results obtained show that FAST-GM should advance the formal modeling of game systems, equipping developers with a powerful toolset for analysis, verification, and optimization of game mechanics throughout the process of creating a video game.