We introduce the Concept-Model-Graph-View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

Cyanobacteria are prokaryotes whose taxonomy follows the same rules of a code (the International Botanical Nomenclature Code, IBNC) built for eukaryotic photosynthetic organisms. Hence, names of cyanobacteria follow the same rules and are assigned to biological entities (species) that should correspond to eukaryotic species. The main difficulty in the current situation is that the species concept in eukaryotes is based theoretically mainly on the biological species concept, that is centered on genetic exchange through sexual reproduction or lack of them. However, as shown, this difference is important from a theoretical point of view, but also in eukaryotes, the boundaries between different species are very rarely checked experimentally by direct observation of sexual barriers and hybridization events. The main concept for species delimitation is hence that related to morphology and, more recently and always in relation to morphology, DNA sequences. The introduction of distances obtained from matrixes of aligned sequences in the framework of a barcoding project provides a quantitative interpretation of species delimitation in relation to genetic distance that can be used both in eukaryotes and prokaryotes. However, the introduction of quantitative criteria needs the definition of distance thresholds to identify the boundaries between different species and, for doing that, it is necessary to test the distance thresholds in models of traditionally defined and recognized species. An alternative approach may be the comparison of the molecular distance (quantitative approach) to data about the capability of strains/species to exchange genetic information. Unfortunately data about this last question is still scarce. The adoption of molecular criteria to check species boundaries based on morphological characters has proved particularly challenging in cyanobacteria: a known example is provided. In conclusion, the only possible approach appears to be the association of molecular data to the increase of available data about the cell structure and the variation thereof in different physiological situations, particularly at the ultrastructural level. A further necessity is the check of the typus for a large number of cyanobacteria species, often based on old basionyms. In many of these cases the typus is often a drawing and more rarely a herbarium specimen or a microscope slide. In many cases an epitypification or a neotypification appears to be necessary.

BackgroundGood management behavior in patients with diabetes mellitus can reduce disease complications and improve quality of life. This philosophical problem is very important for nurses who are able to provide management care for diabetic patients. The purpose of this literature review is to describe the concept, model or theory that can be used in improving the management of diabetes mellitus patients.MethodsThe method used is by searching several studies published through the Scopus database, PubMed, CINAHL, SpingerLink, and web of science (WOS). The search strategy uses a combination of the terms MeSH Terms. The research questions were designed using the principles of the PICOS framework. Selected studies were published from 2017-2022.ResultsA total of 15 studies were included, with 12 using quantitative methodology and 3 were qualitative. The studies were conducted across various countries. The majority of articles used the concept of self-management intervention based on digital-based development and e-health (n=6), Dorothea Orem's self-care deficit nursing theory n=2) and the transtheoretical model (n=2). These concepts, models and theories are able to improve good self-management through lifestyle changes, psychosocial, cultural acculturation and increase in spiritual values (n=6).ConclusionTransitional change focuses on changing the modifiable factors found in the concepts, models and theories of the articles obtained. Future research can be developed to assess the existence of elaborations and modifications based on the philosophy that people with diabetes are responsible for their own health.

The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issue of uncertain runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of the environment in SAS development is ambiguous and abstract. To make this understanding more concrete, we describe the landscape in this area through a systematic literature review (SLR). We examined 128 primary studies and 14 unique environment models. We investigated concepts of the environment depicted in the primary studies and the proposed environment models based on their ability to aid in understanding. This illustrates the characteristics of the SAS environment, the associated emerging environmental uncertainties, and what is expressed in the existing environment models. This paper makes explicit the implicit understanding about the environment made by the SAS research community and organizes and visualizes them.

Type II diabetes (T2D) is a multifaceted metabolic disease influenced by various factors (genetic, epigenetic, environmental, and other). An alarming surge in T2D prevalence is estimated, reaching 700 million cases by 2045 worldwide. This review explores the complex interactions of multiple risk factors throughout life and offers a new perspective on T2D. Genetic and epigenetic influences are described in detail. Prenatal factors, such as exposure to undernutrition and maternal overfeeding, induce lasting epigenetic changes, affecting T2D susceptibility. Furthermore, birthweight is a crude proxy for perinatal programming, with low and high birthweights associated with increased T2D risk. Often overlooked, the early postnatal period also significantly contributes to perinatal programming. Neonatal over-nutrition and feeding choices exert lasting effects on DNA methylation and metabolic outcomes later in life. Stress during this period further exacerbates T2D susceptibility. In continuation, childhood behaviors which are influenced by prior periods contribute substantially to T2D risk. Adverse childhood experiences amplify the risk of many diseases, including T2D. Sleep hygiene throughout life plays a role in metabolic health, influencing insulin resistance and appetite regulation. In adulthood, obesity remains a prominent predictor of T2D, with chronic stress contributing to weight gain and unhealthy lifestyle. This review proposes a novel perspective, considering T2D pathophysiology as a nonlinear process governed by numerous factors and their interactions throughout life. The inherent unpredictability suggests a need for personalized interventions tailored to specific risk profiles, emphasizing the importance of understanding gene-environment interactions and embracing a holistic view of T2D pathogenesis.

Purpose: Social responsibility has been accepted as a core value of nursing profession, particularly in the area of health disparity, but it seems to be difficult to understand for what is meant by social responsibility. The purpose of this study was to explore ways to define the concept of social responsibility of nursing in order to understand the meaning of social responsibility in the field of nursing profession. Methods: The concept analysis process by Walker and Avant was used to clarify the meaning of social responsibility of nursing. Results: Defining attributes of social responsibility of nursing included praxis, as approach to strategizing for social and structural change of inequity, care for an oppressed group which is under social suffering caused by the privilege and power, and action for health policy change in social and political unequal contexts. The antecedents of social responsibility of nursing included reflectivities in identifying personal characteristics, taking seriously the perspective of other vulnerable population, recognizing social environmental status quo (‘fair or unfair’), developing competences in ethical and moral reasoning with social issues. Consequences of social responsibility of nursing were achieving social justice, as equal access to basic human health need in societal level, equal access to effective nursing practice delivery system and development of health promotion policy for world health administrative practices (“health for all”) in nursing. Conclusion: This study provides a guidance to direct future studies through the procedure of identifying conceptual attributes in the context of social responsibility of nursing. Future studies are suggested to develop instruments for social responsibility as well as development of educational programs to promote social responsibility competences in nursing students and nurses.

The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd-Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd-Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of conceptual space, cognitive space, and semantic space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.

Significant efforts in the past decades to teach evidence-based practice (EBP) implementation has emphasized increasing knowledge of EBP and developing interventions to support adoption to practice. These efforts have resulted in only limited sustained improvements in the daily use of evidence-based interventions in clinical practice in most health professions. Many new interven-tions with limited evidence of effectiveness are readily adopted each year - indicating openness to change is not the problem. The selection of an intervention is the outcome of an elaborate and complex cognitive process which is shaped by how they represent the problem in their mind and is mostly invisible processes to others. Therefore, the complex thinking process which support appropriate adoption of interventions should be taught more explicitly. Making the process visible to clinicians increases the acquisition of the skills required to judiciously select one in-tervention over others. The purpose of this paper is to provide a review of the selection process and the critical analysis that is required to appropriately decide to trial or not trial new intervention strategies with patients.

Serendipity favors the prepared mind, but how does the brain make that lucky find? Analyzing the cerebral mechanisms behind an exceptional (albeit trivial) discovery, the author suggests that a combination of ‘concept cells’ and the Global Neuronal Workspace Hypothesis could explain how we make great discoveries.

The SARS coronavirus 2 pandemic affected many areas of public life and industry. This also applies to research particularly that which relies on user test studies. In order to minimize the risk of infection, several aspects of experiment design including the setting might require alteration. In order to develop a step-by-step approach to plan a study with regard to infection protection, an extensive review of the latest research involving the SARS coronavirus 2 pandemic as a blueprint for dealing with other health situations. As a result, a six-step concept was developed that is applicable for user test studies in both stationary rooms and vehicles which can be adapted to the respective circumstances. The infection protection measures determined through research were implemented in the individual sub-steps from study planning to execution. They allow a step-by-step approach to prevent infections in user test studies during a pandemic and in situations where increased hygiene measures are required, in order to continue to carry out user test studies.

Although increasing knowledge about biological systems has advanced exponentially in recent decades, it is surprising to realize that the very definition of Life keeps presenting theoretical challenges. Even if several lines of reasoning seek to identify the essence of life phenomenon, most of these thoughts contain fundamental problem in their basic conceptual structure. Most concepts fail to identify necessary and sufficient features to define life. Here, we analyzed the main conceptual framework regarding theoretical aspects supporting life concepts, such as (i) the physical, (ii) the cellular and (iii) the molecular approaches. Based on ontological analysis, we propose that Life should not be positioned under the ontological category of Matter. Yet, life should be better understood under the top-level ontology of “Process”. Exercising an epistemological approach, we propose that the essential characteristic pervading each and every living being is the presence of organic codes. Therefore, we explore theories in biosemiotics in order to propose a clear concept of life as a macrocode composed by multiple inter-related coding layers. Therefore, we suggest a clear distinction between the concept of life and living beings, a distinction that is not evident in theoretical terms. From the proposed concept, we suggest that the evolutionary process is a fundamental characteristic for life’s maintenance but not to its definition. The current proposition opens a fertile field of debate in astrobiology, biosemiotics and robotics.

The research is seeking for the elements of the Authentic Realism approach in five Australian short stories by Julie Keyes, Judith Ebun, Jess Roberts, Airdre Grant, and Margot Titcher, to which an authentic realist reader would respond. The content analysis method is used in searching for the depth and significance of the texts. Several aspects such as a depiction of the characters, strong female characters, the form and language of the text, the relation of the text to the author and experience are taken to obtain the reader's response by way of an Authentic Realism approach. The findings show that the short stories: My Mother’s New Boyfriend, Souvenir, Heartbreak Hotel, Lovers Knot and Someone Else are proof that women have their own perspectives and reactions. The female characters make their own choice themselves devoid of men’s influence. Despite Jim’s idleness, unwaged condition and dependency Mother remains his girlfriend. Mother’s previous boyfriends are mostly similar to Jim’s characteristics. Lynne told Mark that she has a lump in one of her breast and will soon have a mastectomy. Being disappointed and scared to picture Lynne with one breast, and lopsided, Mark decides to leave her. The female character in Lovers Knot enjoys having an unusual sex life with Gary. Her meeting with Jeff is only a break from her routine. In Heartbreak Hotel, Mum seems to be a typical ordinary wife with a moody husband. But it turns out that she is extraordinary for she handles all the problems single-handed including her sex life. Deirdre decides to take her new post in Perth after Paul snubs her. The decisions are theirs. The choice may not be perfect, but the choice is made. The writing style of the texts shows that the authors are experienced and know very well how to use the form and language to communicate with their (women) readers. Since the theme of the texts is mostly about daily life experience, (women) readers will find no difficulty in reading them; they can even share their feelings and experience with the characters in the texts. Moreover, by using authentic realist approach students of the English Department learn more about women's writings.

Background: People with Down syndrome (DS) have a trisomy of chromosome 21 and marked deficits in verbal memory but relatively preserved visuospatial perception. Verbal memories are related to semantic knowledge. Little is known about the organization of semantic knowledge in people with DS, which was the aim of this study. Aims: The extent of false memory in people with DS was examined. Possible differences in the number of false memories induced by nominal and verbal themes were examined. Methods and Procedures: In the study phase, ten word lists with semantically related associates were presented. In the recognition phase, participants made judgments on whether each word had been represented previously as semantically related lures, old words, and semantically unrelated new words. Outcomes and Results: Differences in the processing of nominal and verbal theme-related lures were observed among people with DS. When processing nominal theme-related lures, people with DS showed delayed false positives. When processing verbal theme-related lures, people with DS showed no differences compared to controls. Similar patterns have been observed in people with DS when processing nominal theme-related associates and verbal theme-related associates. People with DS have delayed recognition of associates. When processing unrelated words, they showed significantly higher false positives than controls for both nominal and verbal eliciting themes. Further analyses of the themes revealed differences in false positives across syntactic categories among groups. Conclusions and Implications: People with DS showed a delayed recognition of associates and deviance in recognizing unrelated words in the nominal and verbal theme categories. People with DS were delayed in terms of nouns, but deviated with verbs in associated and unrelated words. Additionally, people with DS showed distinct patterns in processing five nominal lures and three verbal lures, suggesting that they formed false memories differently based on distinct syntactic categories. Thus, it was concluded that people with DS develop a deviant semantic structure. Category-based interventions for people with DS should be implemented in order to improve their semantic knowledge through lexical connections.

Children are inherently curious about everything they encounter, making the early years a perfect time for STEAM (science, technology, engineering, arts, and math) investigations. An outdoor learning environment can influence STEAM concept formation of preschoolers with intentional design that offer STEAM learning affordances. Despite the rising interest in early STEAM education, there is still limited literature on how the outdoor environment may influence STEAM learning behaviors of preschoolers (3-5 years old). This scoping review intends to evaluate the existing knowledge regarding the physical factors that contribute to STEAM learning affordances in an outdoor environment for children aged 3 to 5. The review included studies from the last twenty years. This scoping review was conducted in accordance with the criteria outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). For this scoping review, 843 citations were discovered across four databases (JSTOR, Scopus, EBSCOhost, and Web of Science), ProQuest, and Google Scholar, and 31 articles were considered eligible for inclusion. Through the synthesis of information from those 31 studies, a list of STEAM learning behaviors of children and STEAM activity-supportive settings were identified that may positively influence preschoolers' STEAM concept development.

The concept of the protein powder dispenser was developed on the basis of the results of analyses, studies and customer requirements. The dispenser was assembled using SolidWorks software, which consists of 63 elements, 14 of which are unique parts that are not available on the market. Each part of the dispenser was carefully designed to be injected into the plastic compound. Polypropylene is the perfect material for this dispenser as it is low density, waterproof, insoluble and extremely resistant to tension and bending. High purity propylene polymer can be processed into a tough, rigid and lightweight plastic with relatively low manufacturing costs, replacing expensive materials. The authors' original contributions are the design and manufacture of a dosing device for protein powder, adjustable in 3 heights to be able to dose in containers of different sizes; the design and integration of a mechanism with a precise dosing system, the components being designed so that the parts in contact with the shaft filled with protein powder form a perfectly closed space; the design and integration of the mechanism for adjusting the height of the dispenser.

The ecosystem is an essential biological concept linking the living and the inanimate and represents the main structural and functional unit of nature. The biodiversity that exists on our planet can be organized into closely interconnected living worlds: acellular (viruses and capsidless genetic elements), prokaryotic (bacteria and archaea), and eukaryotic (all nucleated organisms). I want to highlight the presence of viruses as components of biodiversity because they are often overlooked in many studies despite their essential ecological and evolutionary role. Furthermore, I propose seven distinctive hallmarks that are inherent to any ecosystem: biodiversity, physical environment, hierarchy, interactivity, openness, “homecostasis”, and evolutionary. From the interaction and coupling of these living worlds with the environment (the environmental world), I define the ecosystem as a specific and dynamic ensemble of living and non-living worlds that functions as an open, hierarchical, and evolving system. This complex web of interactions that we call ecosystem can be graphically represented as a triangle reflecting the dynamic equilibrium between all the worlds. Finally, I propose a new way of graphically relating the ecosystem to biodiversity by taking the ecosystem as the nucleus from which all living worlds emerge.

Aging is a major risk factor for the higher incidence and prevalence of chronic conditions, such as cancer, cardiovascular and metabolic diseases, and neurodegenerative diseases. Closely linked with the onset of all these age-related diseases is immunosenescence, a condition defined as progressive reconfiguration of the immune functions leading to an increased susceptibility to infectious diseases, poorer response to vaccination, increased prevalence of cancer, rheumatoid arthritis, and other chronic diseases characterized by a pro-inflammatory state named inflammaging. Given that immunosenescence frames age-related diseases, it is natural to ask how these illnesses are related under the context of innate and adaptative components of the immune system. Knowledge processing by Formal Concept Analysis (FCA) of age-related diseases framed in an immunosenescence multivalued context shows that a hierarchy among these diseases emerges with a subconcept-superconcept nested structure, with two clusters, first made of metabolic diseases, cancer, and cardiovascular diseases. Second metabolic and neurodegenerative diseases and rheumatoid arthritis. Metabolic diseases seem to be a pivot illness and they must be a primary target given their global incidence and prevalence and their cost-effective treatments like regular physical activity, weight loss, healthy diet, stopping smoking, and reducing or managing stress. Another implication of the ontological analysis of age-related diseases is about the nature of immunosenescence, suggesting that initial symptoms of age-related diseases are mainly related to innate immune mechanisms, and developing of severe symptoms of age-related diseases like cancer, cardiovascular or neurodegenerative diseases depends on the activation of acquired immunity processes.

Secure attachment, developed through consistent relationships with attachment figures in childhood, is a crucial factor in fostering healthy interpersonal relationships and a positive self-perception. This research aimed to assess how perceived security, as an indicator of secure attachment, predicts adult self-concept through the mediating effects of resilience and positive self-esteem. A quantitative, cross-sectional, and non-probabilistic study was conducted with 383 participants, of whom 76.2% were females, aged between 17 and 86 years (M = 28.52, SD = 12.23). Correlational analyses revealed significant positive associations between perceived security, self-concept, resilience, and positive self-esteem. Path analysis demonstrated that resilience and positive self-esteem sequentially mediate the relationship between perceived security during childhood and adult self-concept (Total Effect R2 = .34, F(1,381) = 193, p < .001; Indirect effect of sequential mediation b = .02, CI 95% [.01, .04]). Findings suggest that the ability to cope with ad-versity and personal acceptance underlie the impact of perceived security on self-concept. It is recommended to promote not only secure attachment in family interventions but also resilience and self-esteem in programs targeting adolescents, youth, and adults.

The kinetic energy transfer between the atmosphere and oceans, called wind work, affects ocean dynamics including near-inertial oscillations and internal gravity waves, mesoscale eddies, and large-scale zonal jets. For the most part, recent numerical estimates of global wind work amplitude are almost 5 times larger than those reported 10 years ago. This large increase is explained by the impact of the broad range of spatial and temporal scales covered by winds and currents, the smallest of which have only recently been uncovered by increasingly high resolution modeling efforts. However, existing satellite observations do not fully sample this broad range of scales. The present study assesses the capabilities of ODYSEA, a conceptual satellite mission to estimate the amplitude of wind work in the global ocean. To this end, we use an ODYSEA measurement simulator fed by the outputs of a km-scale coupled ocean-atmosphere model to estimate wind work globally. Results indicate that compared with numerical truth estimates, the ODYSEA instrument performs well globally, except for latitudes north of 40∘N during summer due to unresolved storm evolution. This performance is explained by the wide-swath properties of ODYSEA (a 1,700 km wide swath with 5 km posting for winds and surface currents), its twice-a-day (daily) coverage at mid-latitudes (low latitudes), and the insensitivity of the wind work to uncorrelated errors in estimated wind and current.

Purpose: To identify the impact of dealing with COVID-19 patients in clinical areas on nurses' professional self-concept and self-confidence. Background: Professional self-concept is considered a critical factor in the recruitment/retention process in nursing, nursing shortage, career satisfaction, and academic achievements. Professional self-confidence is also a crucial determinant in staff satisfaction, reducing turnover, and increasing work engagement. Design: Descriptive, comparative study. Methods: The study was conducted between February to May 2021 by utilizing a convenience sampling technique. A total of 170 nurses from two facilities were recruited from two COVID-19 and non-COVID-19 designated facilities. The level of professional self-concept and self-confidence was assessed by utilizing the Nurses' Self-Concept Instrument and Self-Confidence Scale. Results: The professional self-concept level among the exposed group to COVID-19 patients was lower than the comparison group, while the professional self-confidence level among the exposed group to COVID-19 patients was similar to the comparison group. On the other hand, the satisfied staff and those who received professional training in dealing with COVID-19 patients reported a higher level of professional self-concept. Conclusions: Dealing with COVID-19 patients has an impact on professional self-concept; the exposure group was lower than those who did not deal with COVID-19 patients, while the professional self-confidence level among the exposed group was similar to the comparison group. Getting professional training in dealing with COVID-19 patients and being satisfied at work were significant factors in improving the professional self-concept. Policymakers should create strategies that target the improvement of professional training in dealing with COVID-19 patients.

In the past ten years several novel hantaviruses were discovered in shrews, moles and bats, suggesting the dispersal of hantaviruses in many animal taxa other than rodents during their evolution. Interestingly, the co-evolutionary analyses of most recent studies have raised the possibility of non-rodents may have served as the primordial mammalian host and harboured the ancestors of rodent-borne hantaviruses as well. The aim of our study was to investigate the presence of hantaviruses in bat lung tissue homogenates originally collected for taxonomic purposes in Malaysia, 2015. Hantavirus specific nested RT-PCR screening of 116 samples targeting the L segment of the virus have revealed the positivity of two lung tissue homogenates originating from Murina aenea bat species. Nanopore sequencing of hantavirus positive samples resulted in partial genomic data from S, M and L genome segments. The obtained results indicate the first molecular evidence for hantavirus in Murina aenae bat species and also the first discovery of a hantavirus in Murina bat species. Sequence analysis of the PCR amplicon and partial genome segments suggests the identified virus may represent a novel species in Mobatvirus genus within Hantaviridae family. Furthermore, our results provide additional genomic data to help extend our knowledge about the evolution of these viruses.

This paper addresses the approach of fully automated intelligent control systems in production organizations, focusing on intelligent production systems research. The overview highlights a gap in control operations ahead of assembly as single-purpose control machines are frequently employed, which is precisely their drawback. Conversely, there are accurate multipurpose measurement centers that do not provide total quality control over the manufactured parts because of the cost and length of the measuring procedure. The study's primary objective was to create an intelligent modular control system, whose concept enables it to control various components. The purpose of the modular intelligent control system is to keep an eye on each module's quality during the pre-assembly phase. This system ensures exact control over the production process of modules by integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms. The technology makes it possible to monitor several parameters and important quality features. Integrated sensors and diagnostic technologies identify possible inaccuracies and disparities, guaranteeing prompt diagnosis of issues in specific modules. The system's intelligent control algorithms streamline the production process and guarantee synchronization between individual modules to achieve consistent quality and performance. One advantage of implementing the solution is that, on average, inspection time is reduced by 40 to 60% as compared to the prior manual inspection approach. In addition, full measurement data archiving was made available, and finally, the human element—which introduced a substantial error rate into this process—was removed. This system also helps to increase the project's overall efficiency, predictability, and safety while allowing for quick adjustments by standards and requirements.

There is some evidence that aspects of nurse-doctor communication are associated with the quality of care and treatment patients receive whilst they are in hospital. To date, no studies have examined stakeholder perceptions of how patient care is influenced by clinical communication between nurses and doctors. We conducted a concept mapping study to generate a deep understanding of how clinical communication impacts patient care. Concept mapping has six phases: preparation, idea generation, structuring, representation, interpretation, and utilization. Twenty patients, 21 nurses, and 21 doctors participated in the study. Brainstorming generated 69 discreet statements about how nurse-doctor communication impacts patient care. The structuring (rating and clustering) phase was completed by 48 participants. The data interpretation workshop selected a five-cluster solution: effective communication, trust, patient safety, impediments to patient care, and interpersonal skills. On the final concept map, the five clusters were arranged in a circle around the center of the map. Clusters were relatively equal in size suggesting, each concept makes a broadly equal contribution to how nurse-doctor communication influences patient care. Our study suggests that there are multiple aspects of clinical communication that impact patient care. Candidate interventions to enhance nurse-doctor communication may need to consider the complex nature of interprofessional working. Registration: This study was prospectively registered with the Open Science Framework (OSF) (https://osf.io/9np8v/) prior to recruiting the first participant.

(1) Background: Resilience has been presented as a potential protective factor to be promoted in difficult experiences in the elderly. However, further clarification of the concept of resilience is needed about this population, as this is of critical interest for nursing care. (2) Aim: To develop the concept of resilience in the elderly to establish the elements that refer to the nursing outcome Personal resilience (1309) from the Nursing Outcomes Classification (NOC), specifically in the elderly. (3) Methods: Concept analysis using Beth Rodgers' evolutionary model. The attributes, antecedents, consequents and empirical elements were described in the integrative review, with searches in PubMed, CINAHL, PsycINFO, LILACS and Embase databases. A total of 2431 citations have been identified and 110 studies were included. (4) Results: The concept of “resilience in the elderly” is composed of two attributes: available resources and positive behaviours and is defined as positive attitudes of older people with the assistance of resources available from experiences of adversity. Conclusion: This analysis and concept development of resilience in the elderly provided sensitive indicators for nursing care in the context of adversity, considering available resources and with positive attitudes during this phase of life span.

This paper deals with the techno-economic study of the hybrid renewable energy system based on energy storage aspect under the form of hydrogen and methane. Indeed, with the intermittency of the renewable energy sources such as photovoltaic and wind energy, several problems of produced energy injection to the power system network can be encountered due to the shortage or the excess of these sources. This situation appeals the use of systems that ensure the stability of network based on the storage of energy surplus into gas using electrolyzer systems, which will be used afterward to cover the eventual shortage. In the present paper, the study of performance of each pathway of methane and hydrogen storage has been performed by the treatment of multiple scenarios via different architecture case studies in an Algerian location. Whereas, the energy produced by the photovoltaic system, the wind energy and the gas micro turbine sources are considered similar in each case. The modeling and simulation of the studied system operation under optimization criteria has been performed in this work, where the main aim is to define the appropriate configuration taking into account the different with low costs of investment, maintenance operation and immediate reactivity with a big storage capacity.

Business processes are continuously evolving in order to adapt to changes due to various factors. One important process drift perspective yet to be investigated is the detection of branching condition changes in the process model. None of the existing process drift detection methods focus on detecting changes of branching conditions in process models. Existing branching condition detection methods do not take changes within the process into account, hence results are inadequate to represent the changes of decision criteria of the process. In this paper, we present a method which can detect branching condition changes in process models. The method takes both process models and event logs as input, and translates event logs into decision sequences for change points detection. The proposed method is evaluated by simulated event logs.

Particles in classical physics are distinguishable objects, which can be picked out individually on the basis of their unique physical properties. By contrast, in quantum mechanics the standard view is that particles of the same kind (``identical particles'') are completely indistinguishable from each other. This standard view is problematic: Particle indistinguishability is irreconcilable not only with the very meaning of ``particle'' in ordinary language and in classical physical theory, but also with how this term is used in the practice of present-day physics. Moreover, the indistinguishability doctrine prevents a smooth transition from quantum particles to what we normally understand by ``particles'' in the classical limit of quantum mechanics. Elaborating on earlier work, we here discuss an alternative to the standard view that avoids these and similar problems. As it turns out, this alternative approach connects to recent discussions in quantum information theory concerning the question of when identical particles can be considered to be entangled.

Development of both marketing and culture sector has a multi-directional nature showing relationships with the concept of sustainable development that should be considered on various levels of management of cultural sector, i.e. on the level of cultural policy of the state or region, and on micro-scale – in cultural institutions. This is because not only natural environment, economy and technology, but also society and culture constitute the area of sustainable development. Considering the assumptions of sustainable development by cultural institutions is related to implementing in this sphere the marketing concepts that are the expression of adoption of market orientation (on culture participant). The objective of the paper is to show the role of marketing in cultural institutions in the context of assumptions of sustainable development concept. The paper is based on literature studies and results of empirical research of quantitative character, which was conducted on a sample of 451 people managing cultural institutions in Poland. The research included general managers, managers and artistic directors, managers of marketing, promotion and sales departments, as well as owners of cultural institutions. Analysis of research results shows that culture participant has the highest position among groups of consumers of actions conducted by cultural institutions and development of offer diversity, comprised in the concept of sustainable development, emerges as factor stimulating development of culture market that is closely related to growth of the quality of cultural institution offer.

Based on historical facts, revisited from a present-day perspective, and on the documented opinions of the scientists involved in the discovery themselves, strong arguments are given in favor of a proposal to include prominent astronomer Vesto Slipher to the suggested addition of Georges Lemaître's name to Hubble's law on the expansion of the Universe, and thus eventually call it Hubble-Lemaître-Slipher's (HLS) law.

The development of an equipment starts from an effective design activity. The concept selection process is an activity that is entailed in the design stage and its relevance in the design process cannot be overemphasized because it informs the choice of optimal conceptual design from a set of alternative design. Hence, there is a need to accrue efforts to the concept selection process because of its importance. This article presents the identification of optimal conceptual design as a Multicriteria decision making model by assessing the suitability of Fuzzy Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS). The Fuzzy MARCOS model was developed to access four alternative conceptual designs of briquetting machines considering eight design features with several sub features. The fuzzy MARCOS model was able to rank the conceptual designs and the results obtained from the decision analysis showed that the Fuzzy MARCOS model was able to rank the designs based on their performance and the final values of the overall utility function. The overall utility function is a based on the utility degree of the conceptual design alternatives in terms of the best and worst designs identified by the model. The utility degree created a platform for comparison on how the design alternatives varies from the best and worst design.

Efficient traffic systems control in large cities with complex traffic management of signalized intersections is a challenging task, particularly when dealing with high traffic volumes. The city of Zagreb faces this challenge, as all administrative and governmental institutions are located in the old city centre, and routes for escorted vehicles have a significant impact on the traffic network. This paper addresses the issue of the impact of unconditional priority for escorted vehicles on the energy efficiency of the urban traffic network in the city of Zagreb. The traffic network model is developed by using the PTV Vissim microsimulation software. The evaluation was conducted with node (delay, queue length, number of stops) and network evaluation parameters (CO2 emission, NOx emission, PM10 emission, and fuel consumption). The results show that unconditional priority has minimal impact on energy consumption and exhaust emission in the observed scenario. This is a significant result considering all actions that have to be done to manage the passage of the escorted vehicles through the traffic network.

Shells, probably like no other product of nature, have played an important role in the history of mankind. The pre-Hispanic civilizations of Ibero-America also used certain type of shells profusely in their religious ceremonies, in particular, in Ecuador there were two species of main importance, the Spondylus princeps and the Spondylus calcifer broadly employed to manufacture ornaments that possess a strong symbolic, religious and social meaning and that were almost exclusively used by ruling classes. Among these ornaments, the faces carved on the Spondylus shells are little known. In the present study, we chose a total of fifteen (15) pieces from the Pastor Restrepo Lince´s archaeomalacology collection to understand the possible uses of these objects, through the interpretation of the gestures represented on the faces, their dimensions, and their geographical distribution in pre-Hispanic Ecuador. To achieve the proposed objective, we approach the present investigation from the perspective of the formal analysis of concepts, which is a mathematical theory of representation of knowledge, finding that these faces carved in Spondylus, were used daily or in special ceremonial occasions and that its use was common in all the regional cultures of ancient Ecuador, from the oldest such as Valdivia, and for more than 2000 years, indicating a long tradition of the use of Spondylus as an object of great symbolic and economic value until the arrival of the Spanish

Our hypothesis that controlled ozone applications interfere into the redox balance of the biological organism (first published in 1998 with a preclinical trial on protecting the liver from CCl4 intoxication), has been verified over the last 2 decades in reactive oxygen species (ROS)-induced mitochondrial pathologies, such as rheumatoid arthritis, osteoarthritis, aging processes, diabetes-2 and in prevention of intoxications. Low-dose ozone acts as a redox bioregulator: The restoration of the disturbed redox balance is comprehensible in a number of preclinical and clinical studies by a remarkable increase of the antioxidant repair markers, here mainly shown as glutathione increase and a reduction in oxidative stress markers, mainly malondialdehyde. The mechanism of action is shown, relevant data are displayed, evaluated and comprehensively discussed: The repair side of the equilibrium increases by 21% up to 140% compared to the non-ozone treated groups and depending on the indication; the stress markers are simultaneously reduced, the redox system regains its balance.

Spiritual security should be a priority in our modern globalized society. Therefore, the study provides convincing evidence that it is important to develop a theoretical understanding of spiritual security. Several theoretical concerns regarding the establishment and maintenance of spiritual security are addressed in the article. He explores many ideas that are interrelated, including spiritual security, worldview, and spirituality. To ensure spiritual security, a practical analysis is carried out of the need to carry out various measures that strengthen the spiritual health of society.The article explains the methodology used in spiritual Security Investigations. According to the author, different levels of spiritual security can be analyzed using a structural-logical model and a dialectical method of cognition. Comparative analysis is also seen as a valuable tool. Spiritual intelligence is essential to overcome barriers, manage change, and ensure personal security and its impact on relationships. The process begins with introspection and evaluation, concluding with the ability to assess and understand the environment. By analyzing and understanding natural phenomena, it is possible to develop a sense of reality and a deeper appreciation of the nature of human existence. A person can feel spiritually safe if he adheres to his moral and spiritual beliefs and complies with the norms of society. It contains such things as worldview, social behavior, culture, research and social development. By expanding the spiritual point of view, the danger to the spiritual security of a person can be reduced.

Positive Health (PH) has been described as a promising transformative innovation to address the challenges of promoting well-being and reducing the burden of disease. The aim of this study is to review the current state of knowledge about PH as introduced by Huber et al. (2016) within the available scientific literature. A rapid review of the literature following the Cochrane Rapid Review recommendations. Three databases were searched (PubMed, Google Scholar, and CINAHL). Data were extracted and synthesized using a narrative approach. A total of 55 articles were included. Initial evaluation studies reveal promising results both at individual and collective levels. However, a need is expressed in several articles for further refinement of the conceptualization of PH and ways to make the effects of the PH approach more measurable. Professionals also expressed a desire for a more informed application and elaboration of the PH method across various settings and populations to enhance its effectiveness in practice. This rapid review highlights the transformative potential of PH in shifting from a disease-focused to a health-oriented paradigm of healthcare. It underscores the need for continued research regarding further development of the concept and its practical method, along with the necessity for methodological innovation.

As breast cancer continues to take a devasting public health toll, most primary prevention approaches are targeted at individual actions. We have proposed, instead, developing systemic, population approaches to preventing the disease. We used a combined qualitative-quantitative methodology, Group Concept Mapping (GCM), to identify Importance and Feasibility ratings of systemic interventions across a wide spectrum of approaches and stakeholders. Participants (n=351) from across the state of California sorted 84 potential interventions into topical piles, and then rated each intervention on perceived Importance and Feasibility. Multidimensional scaling and cluster analysis identified eleven clusters or themes of interventions. Participants rated interventions on Importance and Feasibility differently depending on the region of the state in which they lived. The results of this study underscore the importance of sharing health information with and seeking public health solutions from community partners in general and from beyond the urban areas usually studied.

In this paper, we study properties of formal concepts from an order-theoretic perspective. We first establish a natural correspondence between formal contexts and preorders as well as their induced posets. Then, based on the poset induced from a formal context, we provide characterization for join-irreducible or meet-irreducible elements of finite concept lattices. In addition, we introduce the notion of rough conceptual approximations based on topological closure and interior operators. In contrast with the conventional definition of equivalence class of an object used in Pawlakian approximation spaces, we instead utilize the extent of an object concept. We show that rough conceptual approximations are equivalent to approximation operators in the generalized approximation space associated with the preorder corresponding to a formal context. We also illustrate these theoretical results with examples and discuss their potential applications.

Body Expression (BE) has been defined in the past decades as a discipline within Physical Education (PE) with very particular characteristics and a strong emotional component. In this study, a programme of this discipline was applied to university Physical Activity and Sports Sciences (PASS) students from six consecutive academic years: three prior to and three during the pandemic. A pre-post design was used to determine how the BE programme affected the university students' self-concept (SC). To do so, a questionnaire with a multidimensional approach to this construct was administered, whose dimensions were closely related to the BE programme's characteristics. The results revealed significant improvements in the final SC, compared to the initial SC. Men reported lower SC values than women before the programme implementation, but higher at the end. Therefore, the change was larger in men, so the programme may have had an equalising effect between groups. It was also verified that the pandemic had particularly affected women.

We introduce a modelling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e. the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.

Epistemological naturalism dismisses the notion that epistemology is a basis for the empirical sciences. In particular, it rejects the demand for a general justification of induction. Making inductive generalisations is a basic habit among humans. There is no such thing as a logic of inductive inference. The role of induction in science is heuristic; it is our way of inventing new theoretical predicates and developing theories. We discover new laws by applying inductive thinking; but this is not any kind of inference which can be evaluated as more or less rational.

The first part of this paper contains a brief description of the beginnings of modern cosmology, which, the author will argue, was most likely born in the Year 1912. Some of the pieces of evidence presented here have emerged from recent research in the history of science, and are not usually shared with the general audiences in popular science books. In special, the issue of the correct formulation of the original Big Bang concept, according to the precise words of Fred Hoyle, is discussed. Too often, this point is very deficiently explained (when not just misleadingly) in most of the available generalist literature. Other frequent uses of the same words, Big Bang, as to name the initial singularity of the cosmos, and also whole cosmological models, are then addressed, as evolutions of its original meaning. Quantum and inflationary additions to the celebrated singularity theorems by Penrose, Geroch, Hawking and others led to subsequent results by Borde, Guth and Vilenkin. And corresponding corrections to the Einstein field equations have originated, in particular, R², f(R), and scalar-tensor gravities, giving rise to a plethora of new singularities. For completeness, an updated table with a classification of the same is given.

The article describes the application of the Smart City concept and the economic opportunities it creates, infrastructure and services, and opportunities to improve governance. The main features of the Smart City concept, development directions and evolution, standards and solutions, factors and obstacles to its implementation have been analyzed by the author. The experience of different countries in the application of digital technologies is discussed. The article provides the scope and structure of the "smart" market, application stages and scenarios. International experience in this field was widely analyzed and examples were given, comparisons were made and suggestions were made. The article talks about smart cities, the construction of which has already begun in Azerbaijan. The application of the smart city concept in Azerbaijan has been studied. It is stressed that the spread of digital technologies for the construction of a smart city in Azerbaijan is a prerequisite. The Network Readiness Index (NRI) identifies the indicators that are holding back Azerbaijan in the ranking for 2021. In our study, based on the growth dynamics for 2017-2020, the forecast was calculated by analyzing the trend in the ICT development index in Azerbaijan for 2022-2025.

This paper addresses pastoral resilience by drawing out the coping strategies and mechanisms utilized by the Maasai Pastoralists through a food system approach, based on the study findings of an anthropological study of pastoralism in Laikipia County, Rift Valley, Kenya. This paper is guided by the specific objectives aimed at establishing actors and their roles, and describing the institutional settings and changes in pastoralism. Using a new institutionalism approach, the paper focuses not only on the actors and their roles in pastoralism but also on how internal and external forces regulate access and use of common pool resources (CPRs) resulting in sustainability of the food system. We argue that this has an impact on the practice of pastoralism that continually defines and redefine the actors’ roles as well as elicit the value of pastoral economies and benefits accrued to a wide range of actors hence reinforcing pastoral resilience. The study also identified institutional settings and changes that lead to pastoral survival resulting from the country’s devolved system of governance. Data collection was through in-depth interviews, key informant interviews, focus group discussions and unstructured observations in the pastoral regions. The findings reveal that actors at the household, state, non-state, and service providers have developed varied coping strategies and mechanisms that sustain pastoralism. The study also identified institutional settings and changes that promote pastoral resilience; notably, private land ownership patterns, co-management of livestock markets, commercialization of herding, decentralization of livestock services, holistic management of pasturelands and the use of water-shed management plans. As a result, increased scholarship and advocacy in regards to the concept of co-management of livestock markets, is recommended as a means of understanding pastoral resilience that the food system exhibits.

Economic development and advancements in information technology contributed to the shift on accounting objective from commission responsibility view to decision-making useful view. The decision-making useful view claims that the accounting measurement methods should be changed to improve the usefulness of accounting information, to enable information users to make correct decisions. In addition, the development of financial instruments leads to the birth of fair value accounting. In recent years, fair value has been one of the most important measurement methods in the International Accounting Standard and US Generally Accepted Accounting Principles. However, China has been quite cautious when it comes to applying fair value full time, especially after the global financial crisis happened in 2008, wherein fair value accounting was exposed to strong suspicion. Hence, studying the value relevance of fair value is of great significance to exploring the accounting profession and reforming accounting standards. This paper attempts to investigate the value-relevance of fair value based on the data of listed financial companies and manufacturing companies in China. The results indicate that the fair value information in the financial industry has a strong explanatory influence to corporate share price. In contrast, the fair value information shows limited relevance in the manufacturing industry. Finally, consulting with the results of normative analysis and empirical study, this paper suggests several rational advices for the application of fair value in China.

Since the outbreak of 2019 novel coronavirus (2019-nCoV) at the hardest-hit city of Wuhan, the fast-moving spread has killed over three hundred people and infected more than ten thousands in China¹. There are more than one hundred cases outside of China, affecting a dozen of countries globally². The genome sequence of 2019-nCoV has been reported and fast diagnostic kits, effective treatment as well as preventive vaccines are rapidly being developed³. Initial fast-growing confirmed cases triggered lock-down of Wuhan as well as nearby cities in Hubei Province. Mathematical models have been proposed by scientists around the world to project the numbers of infected cases in the coming days ^4,5. However, major factors such as transportation and cultural customs have not been weighed enough. Our model is not set out for precise prediction of the number of infected cases, rather, it is meant for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors. We hope that our model and simulation would provide more insights and perspective information to public health authorities around the globe for better informed prevention and containment solution.

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. As a result, to monitor the health of individuals affected by these conditions, they must maintain up-to-date health records using digital health informatics apps for surveillance. In this review, we provide an overview of the existing literature on identifying long COVID manifestations through hierarchical classification and the characterization of long COVID by different hierarchical groups based on the Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) in artificial intelligence (AI) to identify long COVID. Knowledge exploration, using the concept map for the clinical pathways of long COVID presented in this paper, provides an overview of the data needed to explore tackling the long-term effect of COVID-19 by integrating innovative cohesive frameworks and designing health informatics-based applications. To the best of our knowledge, this is the first paper to explore the potential incorporation of long COVID as a variable risk factor within a digital health informatics application.

This paper presents the Advanced Syntactic-Graph Convolutional Model (ASGCM), a pioneering approach in Aspect-Based Sentiment Analysis (ABSA) that integrates syntactic dependency features within a graph convolution framework. ASGCM stands out for its novel use of dependency edge encoding and tag-based graph convolutions, providing a fine-grained analysis of sentiments associated with specific aspects in text. This model meticulously captures the intricacies of syntactic structures, thereby offering enhanced precision in sentiment analysis. Notably, ASGCM incorporates a dual-layer graph convolution system: one layer processes syntactic dependencies (edges), while the other interprets semantic roles (tags), ensuring a comprehensive understanding of both structural and contextual elements in text. We rigorously tested ASGCM on multiple datasets, including both English and Chinese languages, and our findings reveal a significant improvement in sentiment classification accuracy compared to existing models. The versatility of ASGCM makes it a robust tool for diverse linguistic environments, setting a new standard for ABSA methodologies.

This study addresses the challenge of objectively quantifying the value of specific views, known to provide psychological and physical benefits, from city center apartments. Unlike subjective evaluations, objective measurement is hindered by limited research and methodology. The research focuses on quantifying visibility by projecting rays from measurement points and determining whether they intersect with unobstructed views, such as the sky and natural elements. Two metrics are proposed to evaluate visibility: the number of view rays and the ratio of view rays to windows. The study analyzed 17 types of residential units in 12 complexes in northern Seoul, South Korea, with the aim of isolating the effect of views on housing prices from other factors. Correlation coefficients were used to assess the relationship between view and housing prices. The results indicated that valuations based on average visibility are more favorable than those based on maximum visibility. The amount of visibility showed a stronger correlation with apartment prices than the number of floors, suggesting the need to measure views across the entire space. The study's methodology can be applied to analyze views in different buildings and urban areas.

Surrogate model based optimization method is widely-used to accelerate the design and optimization process~\cite{marler2004survey}. The input of regression model used in the surrogate model are numbers, which requires users to parametrize the geometries. In this paper, a new parameterization-free surrogate model is introduced and its corresponding uncertainty quantification and sensitivity analysis method are discussed. The input of new surrogate model methods is surface mesh of simulation domain. \Gls{gnns} is used to extract geometric information, and \Gls{cnns} is used to predict contours. This framework bypasses parameterization,as a consequence, uncertainties introduced by manual parameterization is reduced. However, such changes compared with conventional surrogate model methods impose great challenge on uncertainties quantification and sensitivity analysis. Uncertainties quantification in this paper means the error bar of prediction results, which is calculated by Gaussian Process Regression method in current surrogate method. In this paper, a new quantification method achieved by Kullback-Leibler divergence (KLD) is introduced. And the sensitivity analysis is conducted by Automatic Differentiation, which gives a Jacobian matrix of inputs. The method and analysis mentioned above are demonstrated by a low-pressure steam turbine rotator and its exhaust system.

Artificial intelligence systems are increasingly becoming a component of security-critical applications. The protection of such systems from various types of destructive influences is thus a relevant area of research. The vast majority of previously published works are aimed at reducing vulnerability to certain types of disturbances or implementing certain resilience properties. At the same time, the authors either do not consider the concept of resilience as such, or their understanding varies greatly. This work presents a formalized definition of resilience and its characteristics for artificial intelligence systems from a systemic point of view. It systematizes ideas and approaches to building resilience to various types of disturbances. Taxonomy of resilience of artificial intelligence systems to destructive disturbances is proposed. Approaches and technologies for complex protection of intelligent systems, issues of their resource efficiency and other open research issues are considered. Approaches of resilience assessment for artificial intelligence system are also analyzed and recommendations are provided for their implementation.

Owing to the advantages of cost-effectiveness, compactness, and the avoidance of complicated camera synchronization, single-camera three-dimensional (3D) digital image correlation (DIC) techniques have gained increasing attention for deformation measurement of materials and structures. In the traditional single-camera 3D-DIC system, the left and right view images can be recorded by a single camera using a diffraction grating, a bi-prism or a set of planar mirrors. To further improve the measurement accuracy of single-camera 3D-DIC, this paper introduces a single-camera four-view imaging technique by installing a pyramidal prism in front of the camera. The 3D reconstruction of the measured points before and after deformation is realized with eight governing equations induced by four views, and the strong geometric constraints of four views can help to improve the measurement accuracy. A static experiment, a rigid body translation experiment and a four-point bending experiment show that the proposed single-camera 3D-DIC method can achieve higher measurement accuracy than the dual-view single-camera 3D-DIC techniques and has advantages in reducing both random error and systematic error.

Since the Paris Agreement in 2016, the goals of limiting climate change and moving toward climate resilience stand. With a share of about \SI{80}{\percent} of global \ce{CO2} emissions, the energy sector is an essential driver for these goals. A shift to low-carbon energy production and a decentralized system for more efficient energy transmission distribution is necessary. In this paper, we present our work on Modeling of Power Exchanges, Algorithms for \ac{LEM}, Competitiveness of \ac{CHP} and Energy Feedback Devices. The study was conducted considering technical, economic, social and regulatory framework. For easy integration into energy simulations or a \ac{DEMS}, a model for power exchanges was created that allows flexible input or deterministic price patterns. The algorithm handles the clearing of an \ac{LEM} by a district aggregator using limit orders with the goal of increasing the share of locally consumed electricity using economic incentives. An investigation was conducted into the operation of flexible \acp{CHP} in low-carbon power systems to balance the volatility of renewable energy. An energy signal light was developed as an energy feedback device, which is integrated into the \ac{DEMS} in a living lab and allows individual configuration. In summary, the results presented should be compared with those of other research approaches in the future and require qualitative and quantitative evaluation.

In this paper, we give some upper and lower bounds for the multiplicative Randic index, reduced reciprocal Randic index, Narumi-Katayama index and symmetric division index a graph using solely the vertex degrees. Then we obtain upper and lower bounds for these indices for the complete graphs, path graphs and Fibonacci-sum graphs. Finally, we compared the bounds of these indices for a general graph and some special graphs.

We revise the definition of graph operations in [WDK2018] and adapt correspondingly the construction of graph term algebras. As a first contribution to a prospective research field Universal Graph Algebra, we generalize some basic concepts and results from algebras to graph algebras. To tackle this generalization task, we revise and reformulate traditional set-theoretic definitions, constructions and proofs in Universal Algebra by means of more category-theoretic concepts and constructions. Especially, we generalize the concept generated subalgebra and prove that all monomorphic homomorphisms between graph algebras are regular. Derived graph operations are the other main topic. After an in depth analysis of terms as representations of derived operations in traditional algebras, we identify three basic mechanisms to construct new graph operations out of given ones: parallel composition, instantiation, and sequential composition. As a counterpart of terms, we introduce graph operation expressions with a structure as close as possible to the structure of terms. We show that the three mechanisms allow us to construct for any graph operation expression a corresponding derived graph operation in any graph algebra.

The Model-View-Controller (MVC) architecture, a cornerstone of modern software engineering, offers a robust framework for developing dynamic user interfaces. This paper conducts a broad analysis of MVC’s deployment across various sectors, emphasizing its critical role in technological advancement and market integration. By examining implementation strategies in web development, mobile applications, and enterprise solutions, the study illustrates how MVC enhances application manageability, promotes efficient code reuse, and facilitates parallel development. The paper explores the adoption of MVC principles within major technologies such as Java, .NET, and JavaScript frameworks, underlining its widespread acceptance. Additionally, the analysis covers the preferential use of MVC frameworks within diverse market sectors including technology enterprises, educational institutions, e-commerce, and financial services, which leverage MVC for superior data management and user experience. Emerging trends such as the shift towards the Model-View-ViewModel (MVVM) architecture and the integration of MVC with artificial intelligence and machine learning technologies are also discussed. This comprehensive review not only reaffirms MVC’s enduring relevance but also its adaptive evolution in response to emerging technological demands and market needs.

The primary screening of hybridoma cells is a time-critical and laborious step during the development of monoclonal antibodies. Often critical errors occur in this phase, which supports the notion that the generation of monoclonal antibodies with hybridoma technology is difficult to control and hence a risky venture. We think that it is crucial to improve the screening process to eliminate most of the immanent deficits of the conventional approach. With this new microarray-based procedure, several advances could be achieved: Selectivity for excellent binders, high throughput, reproducible signals, avoidance of misleading avidity (multivalency) effects, and simultaneous performance of competition experiments. The latter can directly be used to select clones of desired cross-reactivity properties. In this paper, a model system with two excellent clones against carbamazepine, two weak clones and blank supernatant has been designed to examine the effectiveness of the new system. The excellent clones could be detected largely independent of the IgG concentration, which is unknown during the clone screening since the determination and subsequent adjustment of the antibody concentration is not possible in most cases. Furthermore, in this approach, the enrichment, isolation, and purification of IgG for characterization is not necessary. Raw cell culture supernatant can be used directly, even when fetal calf serum (FCS) or other complex media had been used. In addition, an improved method for the oriented antibody-immobilization on epoxy-silanized slides is presented. Based on the results of this model system, we conclude that this approach should be preferable to most other protocols leading to many of false positives, causing expensive and lengthy confirmation steps to weed out the poor clones.

This article aims to solve the problem of robust supervision of a reverse osmosis desalination system (RO-DS) with two models, external model and bond graph model. The structure of an industrial system from the point of view of the external model operates according to several modes of operation (degraded and normal). For this external model, we cannot locate the faults since we are talking about a global operation of the system. The possible solution for the development of research was to use the multidisciplinary model called bond graph. This model by its graphic nature and using a unified language makes it possible to model the industrial system element by element from where it helps the user not only to detect the faults but also to locate them when they appear in the system. The results suggest that the use of the bond graph model for alarm 02 is a reverse osmosis error (RO1); these phenomena are readable on the bond graph model and can be quantified by equations. The equation of the model of this reverse osmosis (RO1) is found in the residual equations (r5, r6 and r7), so that these residues will be sensitive to this rupture. The possible solution during research development was to use the bond graph model to supplement information with physical knowledge and to locate faults. This article also describes the operating safety (by minimizing false alarms and non-detections as well as delays in fault detection) of the desalination system by using the bond graph model described in Linear Fractional Transformation (LFT) form for enable it to manage the robust supervision of a desalination system. An approach based on (LFT-BG) is developed to monitor tank leakage (Cu) and reverse osmosis (RO1 and RO2) faults or valve level (V1 and V2) closures and membrane clogging reverse osmosis (Rm1 and Rm2) that can occur in the reverse osmosis desalination system (RO-DS).

A signed graph is an undirected graph such that all of its edges are labeled positive or negative. Let D be an embedding of a signed graph G in the Euclidean line. If in D for all of the vertices their positive neighbors are closer than the negative neighbors, then D is called a valid drawing of G. In this paper we show that signed trees, unicyclic graphs, and ladder graphs have a valid drawing in the Euclidean line.

The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.

Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modeling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a number of limitations. The model is implemented using the Neo4j graph database, and a management system has also been developed that allows operations including querying, mapping, and visualizing the stored knowledge in an extended place graph. Then three experimental tasks, namely georeferencing, reasoning, and querying, are selected to demonstrate the superiority of the extended model.

The core reasoning task for datalog engines is materialization, the evaluation of a datalog program over a database alongside its phys- ical incorporation into the database itself. The de-facto method of computing it, is through the recursive application of inference rules. Due to it being a costly operation, it is a must for datalog engines to provide incremental materialization, that is, to adjust the computation to new data, instead of restarting from scratch. One of the major caveats, is that deleting data is notoriously more involved than adding, since one has to take into account all possible data that has been entailed from what is being deleted. Differential Dataflow is a computational model that provides efficient incremen- tal maintenance, notoriously with equal performance between ad- ditions and deletions, and work distribution, of iterative dataflows. In this paper we investigate the performance of materialization with three reference datalog implementations, out of which one is built on top of a lightweight relational engine, and the two others are differential-dataflow and non-differential versions of the same rewrite algorithm, with the same optimizations.

We examine how external triggers, including the digital imperative and the need for more sustainable resource and stakeholder employment, spark the development of transformative sustainable business models. Drawing on the resource-based view and the shared value approach we conceptualize a multifaceted framework that helps to identify key determinants and coherent layers of transformative sustainable businesses models. Our theoretical arguments integrate recent research findings on external dynamics, such as digital technological advances and rising global competitive dynamics, with internal capabilities on both the organizational and the individual level, allowing for a more complete understanding of transformative potentials on the firm level. We propose that key determinants of sustainable transformative business models adhere to both, innovative value-creating reconstructionist and sustainable shared-value logic, and include elements such as co-creation with customers, usage-based pricing, agile and adaptive behavior, closed-loop resource employment, asset-sharing, and collaborative business ecosystems. At the same time, organizational, economic, and environmental layers encompassing sustainable business models need to be both horizontally and vertically coherent to unfold their full potential.

Structure from Motion with Multi-View Stereo photogrammetry (SfM) is increasingly utilised in geoscience investigations as a cost-effective method of acquiring high resolution (sub-meter) topographic data, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via an Unmanned Aerial Vehicle, ‘UAV’) and ground-based (via a handheld digital camera, ‘Ground’) SfM in modelling a hillslope gully system in dry-tropical savanna, and to assess the strengths and limitations of the approach at different scales. A UAV survey covered an entire hillslope gully system (0.715 km²), whereas a Ground survey covered a single gully within the broader system (650 m²). SfM topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate UAV SfM can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., 0.1 m resolution with ~ 0.5 – 1.3 m elevation error), while ground-based SfM is more capable of quantifying gully morphology (e.g., 0.01 m resolution with ~ 0.1 m elevation error). Key strengths of SfM for these applications include: the production of high resolution 3D topographic models and ortho-photo mosaics, low survey instrument costs (< $AUD 3,000); and rapid survey time (4 and 2 hours for UAV and Ground survey respectively). Current limitations of SfM include: difficulties in reconstructing vegetated surfaces; uncertainty as to optimal survey and processing designs; and high computational demands. Overall, this study has demonstrated great potential for SfM to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in tropical savanna landscapes and elsewhere.

Animal models of diseases in particular, the mice, are considered to be the cornerstone for translational research in immunology. The aim of the present study is to model the geometry and analyze the network structure of murine lymphatic system (LS). The algorithm for building the graph model of the LS makes use of anatomical data. To identify the edge directions of the graph model, a mass balance approach to lymph dynamics based on Hagen-Poiseuille equation is applied. It is the first study in which a geometric model of the murine LS has been developed and characterized in terms of its structural organization and the lymph transfer function. Our study meets the demand for quantitative mechanistic approaches in the growing field of immunoengineering to utilize or exploit the lymphatic system for immunotherapy.

This paper considers the principles of building a circuit model of film resistor cutting. The conductive resistive medium is defined with the component equations and the topology of the circuit model. A method of estimating the electric parameters of a resistor operating in the system with a measuring voltage source is shown. An equation system for the node voltages is defined, and the resistive layer parameters are analyzed as the circuit model structure changes during the cutting process.

Neural networks models are mostly represented by oriented graphs where only the components, constitutive elements of the graph, are transcribed into mathematical xpression. Indeed, accurate knowledge of the full expression of the model is required in certain situations such as selecting among several reference models, the one that best fits the available data or comparing the explanatory and predictive performance of an established model with respect to some reference models. In this paper, we establish a formalism of the mathematical expression for multilayer perceptron neural network in a general framework, MLP-p-n-q, with p, n and q natural integers and show its restriction to cases where one has a hidden layer and multivariate outputs (MLP-p-1-q), and then a single output (MLP-p-1-1). Then, we give some specific cases of the most commonly used models. An application case is presented in the context of solving a nonlinear regression problem.

The Randić index of a graph G is the sum of (dG(u)dG(v))−1/2 over all edges uv of G, where dG(u) denotes the degree of vertex u in G. In this paper, we investigate a few graph transformations that decrease Randić index of graph. By applying those transformations, we determine the minimum Randić index on tricyclic graphs, and characterize the corresponding extremal graphs.

Social recommendation is almost as the integration of the business platform and social platform, and gradually become a top in recommendation system. Social recommendation algorithm solves the problem of cold start and data sparseness for traditional commodity, while the internal structure of the relationship graph in social relations has not been fully excavated. This paper proposes two models of Micro Relation Transfer Model and Macro Relation Transfer Model of social relations, and applies the social relations transfer models into the social recommendation system. A relationship graph is built from the relationship between customers on the Internet. Micro Relation Transfer Model establishes the transfer activation function by calculating the relationship between the two customers using the similarity of interests set. Micro Relation Transfer Model spreads the relationship of friends by calculating the proportion of common neighbors held by the customer's social relations. In order to effectively control the transmission range and effect of social relations graph, we introduce pruning algorithm based on Monte Carlo Decision Tree convergence algorithm. The experimental results show that SRRTC algorithm enhances the success rate and stability significantly.

The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory fast graph mutations. Existing graph structures offer a hard tradeoff between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, as well as state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular graph processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed that of several state-of-the-art graph structures, while maintaining low memory consumption when the workload includes updates.

Link prediction is to complete the missing links in the network or to predict the generation of new links according to the current network structure information, which is very important for mining and analyzing the evolution of the network such for construction and analysis of logical architecture in 6G network. Link prediction algorithms based on node similarity need predefined similarity functions, which is highly hypothetical and only applies to specific network structures without generality. To solve this problem, this paper proposes a link prediction algorithm based on the subgraph of the target node pair. In order to automatically learn the graph structure characteristics, the algorithm firstly extracts the h-hop subgraph of the target node pair, and then predicts whether the target node pair will be linked according to the subgraph. Experiments on seven real data sets show that the link prediction algorithm based on target node pair subgraph is suitable for various network structures and superior to other link prediction algorithms.

This paper describes a polynomial time algorithm for solving graph isomorphism and automorphism. We introduce a new tree data structure called Walk Length Tree. We show that such tree can be both constructed and compared with another in polynomial time. We prove that graph isomorphism and automorphism can be solved in polynomial time using Walk Length Trees.

Based on the teaching material of "Robotics" course, this paper studied the automatic knowledge graph construction, including knowledge points extraction and knowledge point relation extraction. We proposed a new method of extracting the first-level, second-level, and third-level knowledge points as well as their prerequisite relations of knowledge points in the textbook. For the problem of insufficient knowledge of the pre-trained language model in the specific field, methods such as incremental pre-training and optimization of cost functions are employed to integrate subject knowledge into the pre-trained language model, thus improving its effectiveness. To overcome the problem that the traditional method of relationship extraction can not be applied directly to the extraction of teaching materials, a new scheme for knowledge point relationship extraction based on keyword relationship is proposed. The experimental data from textbooks shows that the F1 score of knowledge point extraction reaches 93%, considerably improved compared to the traditional model. Consequently, the knowledge point entity extraction and relationship extraction methods based on the pre-trained model can effectively extract structured information and facilitate the automatic construction of knowledge graphs.

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

Wireless sensor networks (WSNs) are a critical research area with numerous practical applications. WSNs are utilized in real-life scenarios, including environmental monitoring, healthcare, industrial automation, smart homes, and agriculture. As WSNs advance and become more sophisticated, they offer limitless opportunities for innovative solutions in various fields. However, due to their unattended nature, it is essential to develop strategies to improve their performance without draining the battery power of the sensor nodes, which is their most valuable resource. This paper proposes a novel sink mobility model based on constructing a bipartite graph from a deployed wireless sensor network. Using the bipartite graph’s properties, the mobile sink node can visit stationary sensor nodes in an optimal way to collect data and transmit it to the base station. We evaluated the proposed approach through simulations using the NS-2 simulator to investigate the performance of wireless sensor networks when adopting this mobility model. Our results show that using the proposed approach can significantly enhance the performance of wireless sensor networks while conserving the energy of the sensor nodes.

Based on the Graph Model of Conflict Resolution (GMCR), a two-stage decision framework is developed to reveal the essence of brownfield incidents and facilitate the resolution of brownfield conflicts caused by the incidents. More particularly, the forward GMCR is utilized in Stage I, the negotiation stage, to simulate the evolution of a brownfield conflict (BC) and predict its potential resolution via stability analysis. If no acceptable equilibrium can be obtained, the BC progresses into Stage II, the third-party-intervention stage, where the inverse GMCR is used to assist a third party in intervening the conflict to achieve a desirable outcome. To illustrate the practicality of this framework, a recent BC occurred in Changzhou, China, is taken as a case study. Invaluable insights are provided through the computation and investigation of the corresponding preference relationships.

Screen viewing time is the total time spent by a child on any digital/electronic device. The objective of the present study was to determine the incidence and predictors of excessive screen viewing time in children in Ujjain, India. This cross-sectional, community-based study was conducted through house-to-house survey by using the three-stage cluster sampling method in 36 urban wards and 36 villages of Ujjain district, India. Excessive screen viewing time was defined as screen viewing for &gt; 2 h/day. The prevalence of excessive screen viewing time was 17.83%. Risk factors identified using the multivariate logistic regression model were: age (OR: 1.5, P &lt; 0.001); mobile phone use before bedtime (OR: 3.17, P = 0.008); parents’ perception about the child habituated to screen (OR: 14.03, P &lt; 0.001); television in bedroom (OR: 48.69, P &lt; 0.001); morning mobile screen viewing time (OR: 9.27, P &lt; 0.001); not reading books other than textbooks (OR: 9.71, P &lt; 0.001); and lack of outdoor play for &gt;2 h (OR: 4.20, P &lt; 0.001). Presence of eye pain was a protective factor for excessive screen viewing time (OR: 0.12, P = 0.011). The study identified multiple modifiable risk factors for excessive screen viewing time.

Molecular descriptors are essential in mathematical chemistry for studying quantitative structure-property relationships (QSPR), and topological indices are a valuable source of information about molecular properties such as size, cyclicity, branching degree, and symmetry. Graph theory has played a crucial role in the development of topological indices and dominating parameters for molecular descriptors. The molecule graph, under graph isomorphism conditions, represents an invariant number, and the graph theory approach considers dominating sets, which are subsets of the vertex set where every vertex outside the set is adjacent to at least one vertex inside the set. The dominating sigma index, a topological index that incorporates the mathematical principles of domination topological indices and the sigma index, is applicable to some families of graphs, such as book graphs, windmill graphs, and some graph operations, which have exact values for this new index. To evaluate the effectiveness of the domination sigma index in QSPR studies, a comparative analysis was conducted to establish an appropriate domination index that correlates with the physicochemical properties of octane and its isomers. Linear and non-linear models were developed using the QSPR approach to predict the properties of interest, and the results showed that both the domination forgotten and domination first Zagreb indices exhibited satisfactory performance in comparison testing. Further research into QSAR/QSPR domination indices is required to build more robust models for predicting the physicochemical properties of organic compounds while maintaining the importance of symmetry.

It is shown that the 5D geodetic equations and 5D Ricci identities give us a way to create a new viewpoint on some problems of Modern Physics, Astrophysics, and Cosmology. Specifically, the application of the 5D geodetic equations in (4+1) and (3+1+1) splintered forms obtained with the help of the monad and dyad methods made it possible to introduce a new, effective generalized concept of the rest mass of the elementary particle. The latter leads one to novel connections between the General Relativity and quantum field theories, as well as all of that, including the (4+1) splitting of the 5D Ricci identities, brings about a better understanding of the magnetic monopole problem and the vital difference in the origins of the Maxwell equations and gives rise to surprising connections between them. The obtained results also provide new insight into the mechanism of the 4D Universe’s expansion and its following acceleration.

Existing Knowledge Graph (KG) models for commonsense question answering present two challenges: (i) existing methods retrieved entities related to questions from the knowledge graph, which may extract noise and irrelevant nodes, and (ii) lack of interaction representation between questions and graph entities. However, current methods mainly focus on retrieving relevant entities with some noisy and irrelevant nodes. In this paper, we propose a novel Retrieval-augmented Knowledge Graph (RAKG) model, which solves the above issues through two key innovations. First, we leverage the density matrix to make the model reason along the corrected knowledge path and extract an enhanced knowledge graph subgraph. Second, we fuse representations of questions and graph entities through a bidirectional attention strategy, in which two representations fuse and update by Graph Convolutional Network (GCN). To evaluate the performance of our method, we conduct experiments on two widely-used benchmark datasets CommonsenseQA and OpenBookQA. The case study gives insight into findings that the augmented subgraph provides reasoning along the corrected knowledge path for question answering.

In this article, there's an effort to make sense about the new versions of matroid. I believe that there's new idea on the background of. matroid. Two styles of matroid is defined in the background of fixed graphs and after that the attributes of these new notion on the graph and its parameters have been studied. The focus of this article is on the version of matroid which has the basis on the cycles as if there's gentle discussion on the results which are based on the set of independent vertices as matroid-x. The relation amid fundamental parameters and specific set like independent set and minimal set in the terminology of graph theory have been considered. Matroid is the word to use in the study on the parameters of graph theory as if set theory and its terminology are also recorded. The terms of word in various terminology have been relatively used. There are open ways to use hypergraphs or some serious relations amid these two types.

In considering knowledge graphs in a diverse range of domains of interest, graph neural networks have demonstrated significant improvements in node classification and prediction when applied to graph representation with learning node embedding to effectively represent hierarchical properties of graphs. DiffPool is a deep-learning approach using a differentiable graph pooling technique that generates hierarchical representations of graphs. In operation DiffPool is a differentiable graph pooling technique that generates hierarchical representations of graphs. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep graph neural network with nodes mapped sets of clusters. However, control of the learning process is difficult given the complexity and large number of parameters on an `end-to-end’ model. To address this difficulty we propose an novel approach termed FPool which is predicated on the basic approach adopted in DiffPool (where pooling is applied directly to node representations). Methods designed to enhance data classification have been developed and evaluated using a number popular and publicly available sensor data sets. Experimental results for FPool demonstrate improved classification and prediction performance when compared to alternative methods. Moreover, FPool shows an important reduction in the training time over the basic DiffPool framework.

Metabolic pathways provide key information to achieve a better understanding of life and all its processes; this is useful information for the improvement of medicine, agronomy, pharmacy, and other similar areas. The main analysis tool used to study these pathways is based on the idea of pathway comparison, using graph data structures. Metabolic pathway comparison has been defined as a computationally complex task \cite{ay2011submap,abaka2013campways}. In previous work from 2017, two different approaches that simplify the problem of comparing pathways represented as graphs were introduced. The first algorithm consists of the transformation of a two-dimensional graph structure, representing a metabolic pathway, to a one-dimensional structure and thus aligning the corresponding data using a reduced 1 dimension string. The second algorithm consists of performing a paired analysis between reactions in pathways and thus eliminating all similarities, finally, showing these differences to the user. The suggestion is to use the information provided by these algorithms as a previous analysis to a deeper, more expensive, comparison tool use. Here we provide an extension of this work with more data and deeper analysis. These methods have shown to be an effective way to treat the problem of metabolic pathway comparison as listed in the discussion and results section. Our results show evidence of a quick, simple and effective way to resolve the described problem.

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

Current effort with light field displays is mainly concentrated on the widest possible viewing angle, while a single viewer only needs to view the display in a specific viewing direction. To make light field display a practical practice, a super multi-view light field display is proposed to compress the information in the viewing zone of a single user by reducing the redundant viewpoints. A quasi-directional backlight is proposed and a lenticular lens array is applied to achieve the restricted viewing zone. Eye-tracking technique is applied to extend the viewing area. Experimental results show that the proposed scheme can present vivid 3D scene with smooth motion parallax. Only 16.7% conventional light field display data are required to achieve 3D display. Furthermore, an illumination power of 3.5 watt is sufficient to lighten a 31.5-inch light field display，which takes up 1.5% of the illumination power required for planar display of similar configuration.

Auto-labeling is one of the main challenges in 3D vehicle detection. Auto-labeled datasets can be used to identify objects in LiDAR data, which is a challenging task due to the large size of the dataset. In this work, we propose a novel methodology to generate new 3D based auto-labeling datasets with a different point of view setup than the one used in the most recognized datasets (KITTI, WAYMO, etc.). The performance of the methodology has been further demonstrated with the development of our own dataset with the auto-generated labels and tested under boundary conditions on a bridge in a fixed position. The proposed methodology is based on the YOLO model trained with the KITTI dataset. From a camera-LiDAR sensory fusion, it is intended to auto-label new datasets while maintaining the consistency of the Ground Truth. The main contribution of this work is a novel methodology to auto-label autonomous driving datasets using YOLO as the main labelling system. The performance of this approach is measured retraining the contrast models of the KITTI benchmark.

Recently, deep learning-based image quality enhancement models have been proposed to improve the perceptual quality of distorted synthesized views impaired by compression and Depth Image Based Rendering (DIBR) process in multiview video systems. However, due to the lack of multi-view video plus depth data, the training data for quality enhancement models is small, which limits the performance and progress of these models. Augmenting the training data to enhance the Synthesized View Quality Enhancement (SVQE) models is a feasible solution. In this paper, we suggest a deep learning-based SVQE model using more synthetic Synthesized View Images (SVIs). To simulate the irregular geometric displacement of DIBR distortion, a random irregular polygon-based SVI synthesis method is proposed based on existing massive RGB/RGBD data, and a synthetic synthesized view database is constructed, which includes synthetic SVIs and DIBR distortion masks. Moreover, to further guide the SVQE models to focus more precisely on DIBR distortion, the DIBR distortion mask prediction network which could predict the position and variance of DIBR distortion is embedded into the SVQE models. The experimental results demonstrate that by pretraining on the synthetic SVI database, the performance of the existing SVQE models could be greatly promoted. In addition, by introducing the DIBR distortion mask prediction network, the SVI quality could be further enhanced.

Re-shaping of thermodynamics with the graph theory and Ramsey theory is suggested. Maps built of thermodynamic states are addressed. Thermodynamic states may be attainable and non-attainable by the thermodynamic process in the system of constant mass. We address the following question how large should be a graph describing connections between discrete thermodynamic states to guarantee the appearance of thermodynamic cycles? The Ramsey theory supplies the answer to this question. Direct graphs emerging from the chains of irreversible thermodynamic processes are considered. In any complete directed graph, representing the thermodynamic states of the system the Hamiltonian path is found. Transitive thermodynamic tournaments are addressed. The entire transitive thermodynamic tournament built of irreversible processes does not contain a cycle of length 3, or in other words, the transitive thermodynamic tournament is acyclic and contains no directed thermodynamic cycles.

The realization of the third-generation artificial intelligence (AI) requires the evolution from perceptual intelligence to cognitive intelligence, where knowledge graphs may not meet the practical needs anymore. Based on the dual channel theory, cognitive graphs are established and developed through coordinating the implicit extraction module and the explicit reasoning module as well as integrating knowledge graphs, cognitive reasoning and logical expressions, which have achieved successes in multi-hop question answering. It is desired for cognitive graphs to be widely used in advanced AI applications such as large-scale knowledge representations and intelligent responses, promoting the development of Al dramatically. This review discusses cognitive graphs systematically and elaborately, including basic concepts, generations, theories and technologies. Moreover, we try to predict the development of cognitive intelligence in the short-term future and further enlighten more researches and studies.

This article addresses the question why initiatives in the field of green business and sustainable development often fail. Therefore, it dismantles some typical patterns of failure and shows – as a case study – how these patterns can be challenged through an innovative educational concept: the green business and sustainable development school. The applied methodology is a real-life project that is designed through blended, interdisciplinary elements from business model canvas, Theory U, participation and design thinking. The results of the school initiative are discussed and evaluated by four distinctive stakeholder groups and outline the school’s supporting potential to overcome typical patterns of failure by the younger generation in the future. This article concludes with ideas to enhance the school concept reaching out to even more stakeholder-groups to increase its reliability and viability.

With recent advancements in network technology and spread of Internet, use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges occur over time. Additionally, owing to the need for efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time.

The dynamic factors in the environment violate the static environment assumption of the SLAM algorithm, leading to a decrease in the accuracy of camera positioning. In recent years, many studies have attempted to address dynamic objects by combining semantic constraints and geometric constraints, but issues such as poor real-time performance, the potential for mistakenly treating humans as rigid objects, and subpar performance in high-dynamic scenes still persist. This paper proposes a dynamic scene visual SLAM algorithm called Dynamic SLAM based on target tracking and multi-view geometry (TKG-SLAM), based on object detection, Kalman filters, and multi-view geometry. This algorithm takes into consideration both real-time performance and algorithm accuracy. It combines semantic constraints and multi-view geometry constraints, selectively running the algorithm in different scenarios, filtering and preserving static points for camera pose estimation. Experimental results demonstrate that, compared to current state-of-the-art dynamic SLAM methods, our approach achieves optimal performance in some scenarios and exhibits stronger real-time capabilities.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

As Christopher Alexander conceived and defined through his life’s work – The Nature of Order – wholeness is a recursive structure that recurs in space and matter and is reflected in human minds and cognition. Based on the definition of wholeness, a mathematical model of wholeness, together with its topological representation, has been developed, and it is able to address not only why a structure is beautiful, but also how much beauty the structure has. Given the circumstance, this paper is attempted to argue for the wholeness as the scientific foundation of sustainable urban design and planning, with the help of the mathematical model and topological representation. We start by introducing the wholeness as a mathematical structure of physical space that pervasively exists in our surroundings, along with two fundamental laws – scaling law and Tobler’s law – that underlie the 15 properties for characterizing and making living structures. We argue that urban design and planning can be considered to be wholeness-extending processes, guided by two design principles of differentiation and adaptation, to transform a space – in a piecemeal fashion – into a living or more living structure. We further discuss several other urban design theories and how they can be justified by and placed within the theory of wholeness. With the wholeness as the scientific foundation, urban design can turn into a rigorous science with creation of living structures as the primary aim.

Nowadays, model checking is applied as an accurate technique to verify software systems. The main problem of model checking techniques is the state space explosion. This problem occurs due to the exponential memory usage by the model checker. In this situation, using meta-heuristic and evolutionary algorithms to search for a state in which a property is satisfied/violated is a promising solution. Recently, different evolutionary algorithms like GA, PSO, etc. are applied to find deadlock state. Even though useful, most of them are concentrated on finding deadlock. This paper proposes a fuzzy algorithm in order to analyze reachability properties in systems specified through GTS with enormous state space. To do so, we first extend the existing PSO algorithm (for checking deadlocks) to analyze reachability properties. Then, to increase the accuracy, we employ a Fuzzy adaptive PSO algorithm to determine which state and path should be explored in each step to find the corresponding reachable state. These two approaches are implemented in an open-source toolset for designing and model checking GTS called GROOVE. Moreover, the experimental results indicate that the hybrid fuzzy approach improves speed and accuracy in comparison with other techniques based on meta-heuristic algorithms such as GA and the hybrid of PSO-GSA in analyzing reachability properties.

Application of the Ramsey Theory to the set of the points placed in the plane is discussed. Consider a set of N points located in the same plane. The straight lines connecting these points are yikx=αikx+βik i,k=1...N. Listed below values of slopes αik are possible: αik&gt;0; αik=0; αik is not defined; αik&lt;0. Following coloring procedure is introduced: we connect the pairs of points for which αik&gt;0, αik=0 or αik is not defined, with the red links, and the pairs of points for which αik&lt;0 place with green links. The suggested coloring procedure enables building of the complete bi-colored graph for any set containing N points located in the same plane. We apply the Ramsey theorem to the complete graph emerging from the suggested coloring. For the set containing N=6 points at least one monochromatic triangle will necessarily appear in the graph. The values of the slopes αik depend on the chosen coordinate system. The rotation of coordinate axes changes the coloring of the graph; however, at least one monochromatic triangle will be present for N=6. The introduced coloring procedure diminishes the order of the symmetry group of the regular hexagon, irrespectively to the orientation of the coordinate axes. We hypothesized that this will be true for arbitrary regular n-polygon, independently on the orientation of coordinate axes. The inverse bi-color Ramsey graphs arise, when we replace red links appearing in the source graph with red ones, and vice versa. The total number of triangles in the “direct” and “inverse” Ramsey graphs is the same. We considered the particular case of the set of points for which αik&gt;0 or αik&lt;0 is true in the given coordinates. In this particular case, the rotation of the Cartesian coordinate axes to the angle θ=π2 yields the inverse complete graph. Generalization of this coloring for an arbitrary number and arbitrary location of the source points is introduced.

It has recently been shown that, contrary to the wide belief that a shift-enabled condition (necessary for any shift-invariant filter to be representable by a graph shift matrix) can be ignored because any non-shift-enabled matrix can be converted to a shift-enabled matrix, such a conversion in general may not hold for a directed graph with non-symmetric shift matrix. This paper extends this prior work, focusing on undirected graphs where the shift matrix is generally symmetric. We show that while, in this case, the shift matrix can be converted to satisfy the original shift-enabled condition, the converted matrix is not associated with the original graph, that is, it does not capture anymore the structure of the graph signal. We show via a counterexample, that a non-shift-enabled matrix cannot be converted to a shift-enabled one and still maintain the topological structure of the underlying graph, which is necessary to facilitate localized signal processing.

Street view images can help us better understand the city environment and potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce parameters and computations of the model while ensuring the segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on its basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+.

Streets are an essential element of urban safety governance and urban design, but they are designed with little regard for possible gender differences. This study proposes a safety perception evaluation method from the female perspective based on street view images (SVIs) and mobile phone data, taking the central city of Guangzhou as an example. The method relies on crowdsourced data and uses a machine learning model to predict the safety perception map. It combines the simulation of women's walking commuting paths to analyse the areas that need to be prioritised for improvement. Multiple linear regression was used to explain the relationship between safety perception and visual elements. The results showed the following: 1) There were differences in safety perceptions across genders. Women gave overall lower safety scores and more dispersed distribution of scores. 2) Approximately 11% of the streets in the study area showed weak perceived safety, and approximately 3% of these streets have high pedestrian flows and require priority improvements. 3) Safe visual elements in SVIs included the existence of roads, sidewalks, cars, railways, people, skyscrapers, and trees. Our findings can help urban designers determine how to evaluate urban safety and where to optimise key areas. Both have practical implications for urban planners seeking to create urban environments that promote greater safety.

The paper is to examine the influence of business innovation, business expansion, product and service development, working capital, machinery and equipment on financing choices in the western part of Nigeria. To determine the effect on financing choices, a logistic regression analysis was used. The results in an impressive manner indicate that entrepreneur, essentially with working capital (WC), machinery and equipment (ME) requirement and business innovation (BI) used internal funding sources while business expansion (BE) and product and service development (PS) lean toward external funding sources and the more established and larger firm utilizes debt financing. The approach and the experiential findings offer an unprecedented degree of investigation from an academic point of view through the previous study on Nigeria entrepreneur. Similarly, the experimental results will strengthen the entrepreneur's knowledge, awareness and perception. Through the capabilities of the entrepreneurs, they can prepare and adapt in accordance with the business condition they conduct business and to help them in their choice procedure regarding the capital structure of their organization in the midst of an interval when the fuss of entrepreneur funding is gradually elicited in the Nigerian climate.