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This paper reflects on the paradigmatic assumptions and ideologies that have shaped algorithmic management research. We identify two sets of assumptions: one about the "ontology of algorithms" (which holds that human resource management [HRM] algorithms are non‐human entities with material agency) and one about the "ontology of management" that HRM algorithms afford (which understands algorithmic management as a form of control for maximizing economic/shareholder value). We explain how these core assumptions underpin existing research of HRM algorithms, causing blind spots that hinder new ways of understanding and studying algorithmic management. After identifying and unpacking the assumptions and blind spots, we offer avenues to overcome these blind spots, allowing for future research based on new ideological assumption grounds that will help move algorithmic management scholarship further in significant ways. [ABSTRACT FROM AUTHOR]

Ethnocentric models of disability have resulted in an ontology that rarely embraces the cultural nuances and social structures of specific countries. There have been increasing calls from scholars to expand understandings of disability beyond this hegemonic focus and develop new ways to do culturally specific and respectful inclusive work. Utilizing a novel methodological approach based on Paulo Freire's "Pedagogy of the Oppressed", we problematized past research and the current knowledge landscape of inclusive physical education (PE) in Japan. We identified that PE teachers were constantly "Grappling with Conflict" regarding their intrinsic desire to include disabled students in PE, and the cultural, institutional demands that instructed them how to teach. We drew upon numerous holistic, cultural, and Globally Eastern philosophies of thought to interpret these findings. Through reinterpreting past research, we developed a future research agenda for improving inclusive PE in Japan to support teachers and students through cultural praxis. [ABSTRACT FROM AUTHOR]

Ontologies play a vital role in organizing and constructing knowledge across various domains, enabling effective knowledge management and sharing. The development of domain-specific ontologies, such as the ONTO-TDM ontology for teaching domain modeling, is essential for providing a comprehensive and standardized representation of knowledge within a given discipline. However, to maximize the usefulness and relevance of such ontologies, it is crucial to automate their population with domain-specific information, reducing manual work and ensuring scalability. This paper presents a novel method for ontology population by extracting and integrating relevant information from diverse sources. The method combines the TextRank algorithm with Word2Vec to enhance keyword extraction, capturing both semantic meaning and textual importance. Keywords are then annotated and used to train a machine learning classifier, which aids in integrating new instances into the ontology. Experiments show that the proposed method achieves a precision of 63.33%, a recall of 61.29% and an F1-score of 62.28%, significantly improving keyword extraction and ontology population accuracy compared to existing methods. This validates the method’s effectiveness in semi-automatically extracting relevant instances from diverse data sources, enhancing the efficiency and accuracy of ontology population, and advancing automated knowledge management in domain-specific contexts. [ABSTRACT FROM AUTHOR]

Following sustained development, numerous palaeontology databases and datasets of various types have been created. However, the lack of a unified standard language to describe knowledge and unclear sharing mechanisms between different databases and datasets has limited the large‐scale integration and application of paleontological data. The knowledge graph, as a key technology for semantic translation and data fusion, offers a possible solution to these challenges. Given the potential of knowledge graphs to overcome these obstacles, this paper presents a practical approach to express paleontological knowledge in a knowledge graph via the resource description framework language. By delving into the structured data associated with calcareous nannofossil biozones (the UC zone, CC zone and NC zone), we propose an ontology to describe the semantic units and logical relationships of paleontological biozones and species and then integrate relevant species records from unstructured research reports to construct a knowledge graph for calcareous nannofossils, that integrates multisource paleobiological data and knowledge reconstruction. Our focus lies in detailing the technical aspects of constructing a paleontological knowledge graph. The results demonstrate that knowledge graphs can integrate semistructured and unstructured paleontological data from various sources. This work aims to assist palaeontologists in building and utilizing knowledge graphs, serving as an initial effort for future paleontological knowledge reasoning. [ABSTRACT FROM AUTHOR]

The article focuses on using diffraction and intra-action as innovative approaches to challenge conventional academic practices in sport science. Topics discussed include critiquing the extractive nature of academic capitalism, rethinking human-centric perspectives in sport science, and exploring the relational dynamics between human and nonhuman agents in sports.

Drawing upon the DeleuzoGuattarian metaphor of the 'rhizome', this paper proposes a literature and evidence review methodology that complements data collection, analysis and reporting methods appropriate to new materialist and post-human ontologies. Rhizomatic review replicates the branching and multiplying, subterranean and subversive, endless flows of affect that produce the social world in these ontologies of becoming and difference. The paper situates rhizomatic review in relation to Deleuze and Guattari's understanding of 'minor science': an approach that rather than attempting to represent the social world 'follows the action'. Rhizomatic review is open-ended, avoids setting inclusion or exclusion criteria, follows links that open up during the research process, explores a literature or evidence across disciplines, and engages in multiple iterations of searching and synthesis. An example of a rhizomatic review is presented, and the paper concludes with reflections on the opportunities afforded by rhizomatic review. [ABSTRACT FROM AUTHOR]

Identifying printing defects is vital for process certification, especially with evolving printing technologies. However, this task proves challenging, especially for micro-level defects necessitating microscopy, which presents a scalability barrier for manufacturing. To address this challenge, we propose an attribute learning methodology inspired by human learning, which identifies shared attributes among seen and unseen objects. First, it extracts defect class embeddings from an engineering-guided defect ontology. Then, attribute learning identifies the combination of attributes for defect estimation. This approach enables it to recognize previously unseen defects by identifying shared attributes, even those not included in the training dataset. The research formulates a joint optimization problem for learning and fine-tuning class embedding and ontology and solves it by integrating natural language processing, metaheuristics for exploration and exploitation, and stochastic gradient descent. In a case study involving a direct-ink-writing process for creating nanocomposites, this methodology was used to learn new defects not found in the training data using the optimized ontology. Compared to traditional zero-shot learning, this ontology-based approach significantly improves class embedding, outperforming transfer learning in one-shot and two-shot learning scenarios. This research represents an early effort to learn new defect concepts, potentially reducing the need for extensive measurements in defect identification. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal "Enterprise Modelling & Information Systems Architectures" discussing the convergence of enterprise modeling and knowledge graphs. It emphasizes the need to reconnect diagrammatic enterprise modeling and engineering-oriented knowledge structuring. The editorial explores the use of knowledge graphs in enterprise modeling and introduces three papers that showcase different views on how enterprise models and ontologies or semantic graphs can support each other. The document aims to demonstrate the growing research on the convergence between enterprise modeling and knowledge graphs. Additionally, there is a list of references for articles and papers related to enterprise modeling, knowledge graphs, and ontology-driven conceptual modeling. [Extracted from the article]

Most workers around the world are part of the precariat, characterized by non-permanent, informal, short-term, low-pay, low-skill, and insecure jobs. While there have been many socio-economic critiques of the negative impacts of precarity on workers, the literature has increasingly asked how precarious workers actually live their lives and how their subjectivities are produced on a daily basis. We contribute to this literature by providing a psychosocial account of the ambivalent experiences of precarious workers. We contend that the interplay of recognition and misrecognition plays a crucial role, as the vulnerable, working subject becomes entangled in a complex web of recognizability. We present insights from 104 in-depth interviews, providing a Lacanian analysis of how precarious workers develop unconscious attachments to neoliberal values that are central to the logic of precarity. Understanding this ambivalence helps us develop a more nuanced view of an ethics of precarious workers' vulnerability. [ABSTRACT FROM AUTHOR]

A domain specific language (DSL) ties the business and technical models, by letting technical developers write programs with the business domain properties. Yet, DSLs are not used due to the cost of developing them. Such cost stems from the needed expertise within both the domain knowledge and language development technicalities for any DSL engineer who would design such a language. This paper proposes a semantic-based DSL development approach that utilizes an ontology as a formal way for domain representation. The domain ontology is semi-automatically transformed into a DSL. Then, an ontology reasoning algorithm provides reasoning services on the DSL structure and the programs developed using such DSL by application developers. Such reasoning services can automatically detect flaws in the DSL design like possible inconsistency or the presence of unsatisfiable or redundant classes thus serving the DSL engineer. The reasoning services can also discover inconsistency or redundant classes in programs built using the designed DSL, thus serving the application developer. The proposed approach was implemented within a language workbench using projectional-editing and was evaluated on two different ontologies from varied domains. The results show correct transformation of the input ontology, valid instantiation of designed application, and efficient reasoning services. [ABSTRACT FROM AUTHOR]

As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets. [ABSTRACT FROM AUTHOR]

Background: Bio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains. Results: We present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies. Conclusions: Simona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems. [ABSTRACT FROM AUTHOR]

Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level. [ABSTRACT FROM AUTHOR]

Functional Neurological Disorders are characterized by sensory-motor or cognitive symptoms. Recent research has revealed their complex nature involving biological, psychological, and social factors. Care requires a multidisciplinary approach, which, to date, has yet to be considered. A Constructivist Grounded Theory study was conducted to understand the reasons behind this, exploring Functional Neurological Disorders diagnosis, communication, and understanding from multiple perspectives (patients and healthcare professionals). The core category was "negotiating Functional Neurological Disorders meanings and care amid a dissatisfying dichotomy," with sub-categories: i) seeking to "word" the disease, ii) exposing reductionism, and iii) a pluralist vision emerging. Diagnosing and communicating Functional Neurological Disorders is a process of negotiating meanings and care that hinges on participants' diverse ontological perspectives regarding the condition. Results highlight the difficulty in finding common ground and achieving mutual understanding among the various viewpoints, creating a challenge in establishing a unified approach to Functional Neurological Disorders care. In this context, only a few healthcare professionals emphasized the potential benefits of increased integration. A shift is required from a reductionist to an integrated biopsychosocial perspective to develop a more cohesive approach. Defining a medical paradigm through dialogue with teams and patients is essential in addressing Functional Neurological Disorders effectively. Furthermore, the required interdisciplinary approach holds the potential to mitigate the dissatisfaction arising from fragmented and compartmentalized care (the "dissatisfying dichotomy") experienced by our participants. It signifies a comprehensive strategy that could address the concerns of all involved parties and enhance the overall quality of care provided. [ABSTRACT FROM AUTHOR]

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some ontological research in other dialects, a semantic web ontology for the Badini dialect remains conspicuously absent. This paper addresses this gap by presenting a methodology for constructing and utilizing a semantic web ontology for the Badini dialect of the Kurdish language. A Badini annotated corpus (UOZBDN) was created and manually annotated with part-of-speech (POS) tags. Subsequently, an HMM-based POS tagger model was developed using the UOZBDN corpus and applied to annotate additional text for ontology extraction. Ontology extraction was performed by employing predefined rules to identify nouns and verbs from the model-annotated corpus and subsequently forming semantic predicates. Robust methodologies were adopted for ontology development, resulting in a high degree of precision. The POS tagging model attained an accuracy of 95.04% when applied to the UOZBDN corpus. Furthermore, a manual evaluation conducted by Badini Kurdish language experts yielded a 97.42% accuracy rate for the extracted ontology. [ABSTRACT FROM AUTHOR]

Event extraction is a complex and challenging task in the field of information extraction. It aims to identify event types, triggers, and argument information from the text. In recent years, overlapping event extraction has attracted the attention of researchers because of its higher challenge and practicability, and some work has carried out in-depth research on overlapping event extraction and achieved remarkable results. But these works (1) ignore the role of ontology knowledge in event extraction; (2) use the same semantic encoding for multi-stage models, lacking consideration for the independent characteristics of extraction tasks such as event types, triggers, and arguments; and (3) face issues in the training process of multi-stage models, such as error cascading and slow convergence. To address the above issues, we propose an ontology-guided and scheduled-sampling approach for overlapping event extraction, termed as OGSS. First, we design a symmetric matrix for event ontology knowledge representation and integrate it into the semantic encoding process, infusing ontology knowledge into event extraction. Second, for extraction targets such as event types, triggers, and arguments, we process the semantic encoding according to the characteristics of each extraction target, obtaining semantic representations tailored for each subtask. Finally, we view multi-stage predictions as sequential outputs of a joint model, using a scheduled sampling strategy between subtasks to effectively mitigate the cascading propagation of errors during training and accelerate model convergence. We conduct extensive experiments on the FewFc event extraction benchmark dataset. The results show that OGSS achieves significant improvements in overlapping event extraction tasks compared to previous methods. [ABSTRACT FROM AUTHOR]

The objective of this article is to illustrate the importance of the role philosophy and its tools play in the designing and undertaking of nursing research and its importance to be understood by the practicing researcher to ensure the selected methodology and the tools used provide the framework for obtaining reliable and valid answers to their research questions. The article discusses the three major paradigms (Positivism, Interpretivism, and Pragmatism) which have characterized much of health-related research together with a set of essential and practical tools with no metaphysical assumptions that will hopefully provide an explicit framework and a nomenclature which can be applied as we proceed through the research process. [ABSTRACT FROM AUTHOR]

Background: Osteosarcoma (OS) is undeniably a formidable bone malignancy characterized by a scarcity of effective treatment options. Reprogramming of amino acid (AA) metabolism has been associated with OS development. The present study was designed to identify metabolism-associated genes (MAGs) that are differentially expressed in OS and to construct a MAG-based prognostic risk signature for this disease. Methods: Expression profiles and clinicopathological data were downloaded from Gene Expression Omnibus (GEO) and UCSC Xena databases. A set of AA MAGs was obtained from the MSigDB database. Differentially expressed genes (DEGs) in GEO dataset were identified using "limma." Prognostic MAGs from UCSC Xena database were determined through univariate Cox regression and used in the prognostic signature development. This signature was validated using another dataset from GEO database. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, single sample gene set enrichment analysis, and GDSC2 analyses were performed to explore the biological functions of the MAGs. A MAG-based nomogram was established to predict 1-, 3-, and 5-year survival. Real-time quantitative polymerase chain reaction, Western blot, and immunohistochemical staining confirmed the expression of MAGs in primary OS and paired adjacent normal tissues. Results: A total of 790 DEGs and 62 prognostic MAGs were identified. A MAG-based signature was constructed based on four MAGs: PIPOX, PSMC2, SMOX, and PSAT1. The prognostic value of this signature was successfully validated, with areas under the receiver operating characteristic curves for 1-, 3-, and 5-year survival of 0.714, 0.719, and 0.715, respectively. This MAG-based signature was correlated with the infiltration of CD56dim natural killer cells and resistance to several antiangiogenic agents. The nomogram was accurate in predictions, with a C-index of 0.77. The expression of MAGs verified by experiment was consistent with the trends observed in GEO database. Conclusion: Four AA MAGs were prognostic of survival in OS patients. This MAG-based signature has the potential to offer valuable insights into the development of treatments for OS. [ABSTRACT FROM AUTHOR]

Background: The use of genetic testing in pediatric patients with chronic kidney diseases (CKD) has increased exponentially in the past few years, particularly with the emergence of novel sequencing techniques. However, the genetic yield remains unexpectedly low in nephrology, with an impact on diagnosis, prognosis and treatment. Moreover, the increasing diversity of genetic testing possibilities can be seen as an obstacle by clinicians, in the absence of a strong background in genetics. Here, we propose a step-by-step, multidisciplinary strategy for the diagnostic evaluation of pediatric patients with CKD, and appropriate genetic test selection to maximize the yield of genetic testing. Methods: A total of 126 pediatric patients were enrolled in a retrospective file analysis. Genetic testing techniques used included phenotype-associated next-generation panel sequencing (N = 41), Sanger and SNaPshot sequencing (N = 3) and/or whole exome sequencing (N = 2). Results: Overall genetic yield reached 63% and genetic testing significantly impacted patient management in 70%. The distribution of kidney diseases among patients was balanced and matched previously described pediatric cohorts in terms of glomerulopathies, tubulopathies and ciliopathies. Genetic analyses led to significant treatment modifications, kidney biopsy sparing and personalized nephroprotection, as well as tailored genetic counseling. Of note, the evaluation of Human Phenotype Ontology term accuracy in the cohort showed that causal mutations were precisely identified in 85% of the patients at most. Conclusion: Here we suggest a step-by-step, multidisciplinary strategy to maximize the yield of genetic testing in pediatric patients with CKD. This approach optimizes patient care while avoiding unnecessary treatments or procedures. [ABSTRACT FROM AUTHOR]

The growing concern over high-profile violence has led to a widespread adoption of intelligent smart video surveillance systems in educational institutions. However, accurately identifying abnormal events in the video stream remains a complex task due to the absence of a definitive generic definition for abnormal actions, heavily reliant on contextual factors. In this paper, we propose a fog-assisted abnormal motion recognition system utilizing an ontology-based semantic algorithm to bolster security within university campuses under real-time surveillance. Our ontology-based semantic analysis is meticulously designed to characterize intricate spatio-temporal interactions and contextual relationships in the video scene, thereby significantly improving the sensitivity in distinguishing abnormal events based on their severity level. To ensure swift responses to specific abnormal events, the video streams captured through surveillance cameras are processed at the edge of the network. Prior to semantic analysis, two crucial steps—foreground object segmentation and object tracking—are executed to streamline the detection process. These steps involve segmenting the target foreground using a connected components labeling algorithm and tracking motion patterns based on velocity, direction, and acceleration, employing the Kalman filter. The rule-based reasoning of the ontology accurately defines abnormal conditions in the video scene, providing a clearer understanding of the decision-making process. Furthermore, we incorporate a context-aware refinement step aimed at enhancing detection accuracy. This step distinguishes abnormal events based on their severity and generates corresponding alerts. We conducted extensive experiments and evaluated the proposed system using various measures, demonstrating its potential in real-time video analysis. The results showcase an impressive prediction success ratio of 0.989, affirming the reliability and robustness of our system in detecting abnormal events. [ABSTRACT FROM AUTHOR]

The whole train design of an Electric Multiple Unit (EMU) involves multiple domains and scenarios, thus requiring comprehensive consideration of various factors during the design process. Traditional design methods often utilize text-based approaches to model systems; however, such documentation-based designs often suffer from semantic heterogeneity, inconsistent data sources, and also struggle to provide a more intuitive overview of the overall design process. To address these issues, this paper proposes a method based on ontology–knowledge collaborative drive to achieve integration of the overall EMU design. Firstly, we employ the System Modeling Language (SysML) to construct the Model-Based Systems Engineering (MBSE) model of the EMU, establishing functional and physical architecture element models, with the EMU MBSE model serving as input. Subsequently, in the requirement model, architecture model, and traceability model, we utilize top-level ontology to construct the EMU ontology framework in a top-down manner. Lastly, leveraging the Neo4j database, we employ a knowledge graph (KG) approach to fill domain knowledge into each model in a bottom-up manner, thereby realizing the ontology–knowledge collaborative drive for the overall EMU design construction. The effectiveness of the proposed method is validated using the EMU Passenger Information System (PIS) and Traction transformer System (TS) as examples. [ABSTRACT FROM AUTHOR]

The Appreciative Paradigm, as any paradigm in social sciences, has an original and distinctive way of valuing (Axiology) what is most important in human beings and the social and natural world in which we live. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the regulatory properties of 26 (twenty-six) genes associated with nonsyndromic male infertility. We applied an in silico analysis in order to determine the number and distribution of promoters and identify relevant promoter consensus sequences and potential transcription factors. Underlining the concept of alternative transcriptional initiation (ATI), we have found that 65.4% of genes associated with nonsyndromic male infertility have 1 (one) to 6 (six) promoters, located in the region 1 kb upstream of the TSS, and 41% of them are located at a position below −500 bp. Although the TATA box consensus sequence TAWAAA, such as W is A or T, appears at a common location in all genes, it is shifted for at least 10 bp in the EFCAB9 gene. The C2H2 zinc finger is found to be the most significant common transcription factor, binding genes' promoters GLIS1, ZSCAN21, GLIS3, GLIS1, ZNF770, ZNF780A, ZNF81, and ZNF264. On the other hand, basic leucine zipper factors (bZIPs) bind the JUNB gene promoter specifically, exhibiting unique regulatory properties of all genes associated with nonsyndromic male infertility. Two genes, NANOS1 and ZMYND15, are expected to be less susceptible to DNA methylation, due to the high density of CpG content found in their promoter regions. [ABSTRACT FROM AUTHOR]

The healthcare industry is increasingly reliant on access to large volumes of patient data, which is critical for improving patient outcomes, reducing costs, and ensuring regulatory compliance. However, there are significant challenges facing healthcare organizations in retrieving patient data from databases and ensuring the security and privacy of this data. Addressing these challenges is vital to the future success of the healthcare industry. This paper proposes two solutions to these challenges: a simplified ontology-based retrieval algorithm for healthcare multimedia data to enhance the retrieval process, and Hierarchical learning: ensemble model-based reinforcement learning to improve patient data security. An ontology-based retrieval algorithm was a method used to retrieve relevant information from large datasets. It filtered out irrelevant data and provided contextually relevant results. Hierarchical learning involved organizing learning tasks in a hierarchical structure, with higher-level policies guiding lower-level policies. Ensemble model-based reinforcement learning combined multiple models to improve the performance of a reinforcement learning system. The Simplified Ontology-Based Retrieval Algorithm had excellent performance with an accuracy of 92%, making it highly effective in information retrieval tasks. The paper highlights the importance of using advanced technologies and prioritizing cyber security in healthcare to maintain trust and optimal patient care. Cutting-edge technologies transformed healthcare by enhancing patient data retrieval and cyber security. Examples included encrypted computations, multi-party privacy, query result anonymization, real-time monitoring through IoMT, and secure data sharing. Adopting these solutions ultimately contributed to the protection of sensitive patient data and the overall security of healthcare organizations. [ABSTRACT FROM AUTHOR]

Simple Summary: The current investigation identifies differentially expressed genes that specifically influence the serous subtype of ovarian cancer. This subtype accounts for around three-quarters of ovarian cancer cases. To identify these genes, transcriptomic profiles of serous ovarian cancer and non-cancerous tissue samples were extracted from the Gene Expression Omnibus. Differentially expressed genes were derived using GEO2R tool analyses; genes consistently found among upregulated genes in these profiles were considered to be a serous gene set. Next, the serous gene set was examined for its ontological function using the Molecular Signatures Database and its mutational impact on the gene expression profile of high-grade serous ovarian (HGSO) adenocarcinoma. Results showed that 26 genes are amplified in over 5% of HGSO cancer patients, and many of these amplified genes are related to the cell cycle. These cell cycle-related genes were also identified as being involved in the recurrence of serous ovarian cancer. Overall, this study identifies genes that are potential prognostic markers for serous ovarian cancer. The objective of this study was to identify differentially expressed genes and their potential influence on the carcinogenesis of serous-type ovarian cancer tumors. Serous cancer is an epithelial ovarian cancer subtype and is the most common type of ovarian cancer. Transcriptomic profiles of serous cancer and non-cancerous datasets were obtained from the Gene Expression Omnibus (GEO-NCBI). Differentially expressed genes were then derived from those profiles; the identified genes were consistently upregulated in three or more transcriptomic profiles. These genes were considered as the serous ovarian cancer gene set for further study. The serous gene set derived from the transcriptomic profiles was then evaluated for ontological functional analysis using the Molecular Signatures Database. Next, we examined the mutational impact of this serous gene set on the transcriptomic profile of high-grade serous ovarian (HGSO) adenocarcinoma using the cBioPortal database. Results from OncoPrint revealed that 26 genes were amplified in more than 5% of HGSO cancer patients. Interestingly, several of these genes are involved in cell cycle processes, including genes ATPase family AAA domain containing 2 (ATAD2), recQ-like helicase 4 (RECQL4), cyclin E1 (CCNE1), anti-silencing function 1B histone chaperone (ASF1B), ribonuclease H2 subunit A (RNASEH2A), structural maintenance of chromosome 4 (SMC4), cell division cycle associated 20 (CDC20), and cell division cycle associated 8 (CDCA8). The receiver operating characteristic (ROC) curve results also revealed higher specificity and sensitivity for this subtype of tumors. Furthermore, these genes may affect the recurrence of serous ovarian carcinogenesis. Overall, our analytical study identifies cell cycle-related genes that can potentially be targeted as diagnostic and prognostic markers for serous ovarian cancer. [ABSTRACT FROM AUTHOR]

PPIs play a significant function in many biological processes. In many different areas, DL algorithms have delivered excellent results, but PPI prediction is one where they fall short. To offer a sequence-based prediction of PPI, this work employs a deep ensemble model. In the beginning, traits including "enhanced semantic similarity, features based on gene ontologies, and sequence-based features" are extracted. A deep ensemble model is introduced that combines models like Deep Convolutional Neural Network (DCNN), Recurrent Neural Network (RNN), Deep Max out (DMO), and Deep Belief Network (DBN)" is then used to predict the outcomes of the retrieved features. To improve the prediction model, the training is done by the Chaotic Initialized COOT Optimization Algorithm (CI-COA) by optimizing the training weights of DCNN. The performance of the chosen strategy is finally shown through several measures. [ABSTRACT FROM AUTHOR]

The fast growth of the web generates a significant amount of heterogeneous information such as images, text, audio, and video through various applications. These applications use different layouts to represent significant information. The layouts of table information are overloaded with anomalies that have given rise to intensive research into the semantification of web content and organizing tabular data for knowledge sharing and acquisition. Moreover, there are many anomalies present in tabular layouts that lead to the lack of semantic representation in tabular form and new challenges in data modeling. In this paper, we have discussed the various anomalies present in the tabular data that pertain to ontology learning and population tasks and provide the semantification of tabular data. To complete this task, (1) we provide the list of anomalies that pertain to semantification and provide the resolution to anomalies along with the semantification of tabular data, and (2) we have established the algorithm to interpret the table structure into a formal representation to analyze anomalies and provide the resolution. Furthermore, the proposed approach has been compared with existing approaches using ontology elements, the ability to resolve the anomalies, and the time complexity of the ontology population. [ABSTRACT FROM AUTHOR]

Introduction: To identify the differentially expressed genes of acute myeloid leukaemia (AML) and construct and verify a survival prognosis model combined with patient survival information. Methods: The TARGET database was searched to identify differentially expressed peripheral blood genes in children with AML and healthy children. A gene set functional analysis and pathway analysis were performed using gene ontology and the KEGG pathway. A prognostic model for children with AML was constructed using univariate Cox, LASSO Cox regression and multivariate Cox regression analyses. Time‐dependent receiver operating characteristic (ROC) curves were adopted to assess the predictive capacity of the prognostic models. Results: In total, 1640 differentially expressed genes were screened (1119 upregulated and 521 downregulated genes). The differentially expressed genes were mainly involved in nutrient metabolism and cytochrome P450 metabolism. Six key genes related to the prognosis of AML, FAM157A, GPR78, IRX5, RP4‐800G7.1, RP11‐179H18.5 and RP11‐61N20.3, were identified. Kaplan–Meier curves indicated that 3‐year and 5‐year overall survival was significantly higher in the low‐risk group than in the high‐risk group. The area under the ROC curve was 0.722. At different stages of AML, FAM157A and RP4‐800G7.1 exhibited significant differences in expression. The expression levels of FAM157A were significantly decreased in AML, whereas the expression levels of GPR78, IRX5, RP4‐800G7.1, RP11‐179H18.5 and RP11‐61N20.3 were significantly increased in AML. Conclusion: A prognosis‐related gene model of AML was successfully constructed, and the expression levels of the model genes varied with AML stage. [ABSTRACT FROM AUTHOR]

Inhibiting breast cancer stem cell (BCSC) signaling pathways is a strategic method for successfully treating breast cancer. Nobiletin (NOB) is a compound widely found in orange peel that exhibits a toxic effect on various types of cancer cells, and inhibits the signaling pathways that regulate the properties of BCSCs; however, the effects of NOB on BCSCs remain elusive. The purpose of this study was to determine the target genes of NOB for inhibiting BCSCs using in vitro three-dimensional breast cancer cell culture (mammospheres) and in silico approaches. We combined in vitro experiments to develop mammospheres and conducted cytotoxicity, next-generation sequencing, and bioinformatics analyses, such as gene ontology, the Reactome pathway enrichment, network topology, gene set enrichment analysis, hub genes selection, genetic alterations, prognostic value related to the mRNA expression, and mRNA and protein expression of potential NOB target genes that inhibit BCSCs. Here, we show that NOB inhibited BCSCs in mammospheres from MCF-7 cells. We also identified CDC6, CHEK1, BRCA1, UCHL5, TOP2A, MTMR4, and EXO1 as potential NOB targets inhibiting BCSCs. NOB decreased G0/G1, but increased the G2/M cell population. These findings showed that NOB is a potential therapeutic candidate for BCSCs treatment by regulating cell cycle. [ABSTRACT FROM AUTHOR]

This article describes a conceptual and methodological approach to integrating functional information into an ontology to categorize mental functioning, which to date is an under-developed area of classification, and supports our work with the United States (U.S.) Social Security Administration (SSA). Conceptualizing and defining mental functioning was paramount to develop natural language processing (NLP) tools to support our use case. The International Classification of Functioning, Disability, and Health (ICF) was the framework used to conceptualize mental functioning at the activities and participation level in clinical records. To address challenges that arose when applying the ICF as to what should or should not be classified as mental functioning, a mental functioning domain ontology was developed that rearranged, reclassified and incorporated all ICF key components, concepts, classifications, and their definitions. Challenges emerged in the extent to which we could directly align components in the ICF into an applied ontology of mental functioning. These conceptual challenges required rearrangement of ICF components to adequately support our use case within the social security disability determination process. Findings also have implications to support future NLP efforts for behavioral health outcomes and policy research. Mental functioning in everyday life is an important area of inquiry from the perspectives of public health, health policy, healthcare, and overall individual level health and well-being. A domain ontology of mental functioning that defines concepts and their relationships, and provides a common terminology with definitions, would enable interdisciplinary communication, research, and collaboration. A clearer conceptual model of mental functioning can improve the development of software that can identify, codify, and organize mental functioning information within clinical records into data that can be analyzed. The International Classification of Functioning, Disability and Health was utilized to conceptualize mental functioning and to guide the development of a proposed domain ontology of mental functioning. [ABSTRACT FROM AUTHOR]

The ethics of artificial intelligence (AI) is a research topic with both theoretical and practical significance. However, the ethical and moral aspects of conceptual, ontological, semantic, and knowledge modeling, more specifically, and which are sometimes found in AI applications, is not being given sufficient attention. I argue that it should. Whether considering using or developing these meaning-focused models, there are ethical aspects. This paper offers a preliminary outline about this potentially new research field, discussing: some questions and areas of concern, their significance; scenarios of ethical import, and initial ethical guidelines. A focus area, potential case study, and lens through which to see the ethical significance at hand, is ontologies, with emphasis on the most abstract ontologies (so-called generic, foundational, top-level or upper ontologies). This would aim to show the ethical import of highly abstract (often philosophical) ontological choices, assumptions, and their effects on data, developers, users, and society. In particular, I stress the need for critical examination of such models, their content, their intended meanings, assumptions, ideologies, as well as the development process. Both the ethics of, and ethics in, these activities are, therefore, of interest. Topics in the ethics of AI that are relevant include: transparency, bias, data-sharing, privacy, control over data, etc. This paper thereby contributes to the ethics of AI, as well as the ethics of information, of knowledge managment and relevant topics by developing and discussing the ethics of ontology (and similar systems). Future development will refine and add to the ideas expressed in this paper. [ABSTRACT FROM AUTHOR]

An editorial is presented the Journal of Evaluation in Clinical Practice's role in fostering interdisciplinary debate on practical healthcare issues. Topics include the evolution of discussions on evidence-based practice, the journal's commitment to exploring health philosophy, and the ongoing critical examination of assumptions influencing clinical decision-making.

Components of the biosphere, dynamic and diverse as they are, can be imagined to assume two rather distinct morphologies. Some present themselves as materially bounded unities, while others lack material boundaries but can appear unified conceptually. In this series of two articles on the ontology (the nature of being or the general features of the object/s of enquiry and also their relationships) of biological phenomena, the development of thought regarding boundedness in biology and the nature of bounded part-whole constructions are discussed. In the first part, I discuss some of the classical developments of biological thought that imagined the biosphere as a collection of material entities that come into being due to functions of components within their material boundaries. I present three instances in early conventional bioscience learning, two of these discussing the biosphere as hierarchical unities and one positing internal mechanisms as being crucial to their material ontology. This pedagogical exercise is intuitive and provides focus to students' imagination. I take recourse to Robert Frost's poem "Mending Wall" to guide discussions on the topic: He is all pine and I am apple orchard. My apple trees will never get across. And eat the cones under his pines, I tell him. He only says, "Good fences make good neighbors." He will not go behind his father's saying. And he likes having thought of it so well. He says again, "Good fences make good neighbors." - An extract from Robert Frost's "Mending Wall" [1]. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, jails were advised to reduce facility census, particularly the growing population of those with medical/behavioral health vulnerabilities that increased susceptibility to adverse outcomes. Although jail census decreased across the nation in the initial days to months following pandemic declaration, there are minimal data regarding the health status of those who remained in jail. The current investigation aspired to describe jail census trends before/since the onset of COVID-19 and offer snapshots of temporal changes and context for prevalence estimates of medical/behavioral health conditions in jail detainees from 2019 to 2023. Using a serial cross-sectional design, prescription information for individuals residing in 18 jails across the United States on June 30 of each respective year was extracted and categorized using MediSpan's ontological system to determine prevalence estimates of prescribed agents/products. Although data evidenced an initial 31% census reduction (followed by gradual return to prepandemic rates), prescribing patterns for all major therapeutic drug classes steadily increased, with 10% more individuals prescribed at least one agent in 2023 than 2019. The largest increases were observed for behavioral health agents (e.g., 32.4% of the sample was prescribed psychotropic agents in 2023 compared with 25.7% in 2019). We provide considerations for future investigations. [ABSTRACT FROM AUTHOR]

An important step in implementing geospatial digital twins is to enhance the expressiveness of virtual geographical scenes for the physical world. However, the existing virtual geographical scenes cannot quickly express the dynamically changing geographic environment for remote users due to the inefficient handling of modeling processes, user perception, and remote sharing. The research analysed the concept and characteristics of geospatial digital twins, and constructed the virtual geographical scene ontology, based on which we developed geographical spatiotemporal semantic rules and designed a dynamic annotation algorithm to enhance the representation of virtual geographical scenes. Finally, we investigated a real-time transmission method of panoramic video based on 5 G and used immersive virtual reality (IVR) to realize the user experience of remote immersion in geographical scenes. We selected a specific geographic environment containing multiple typical geographic entities to develop three prototype systems for experimental analyses. The results showed that the proposed method enabled users to view the virtual geographical scene on a VR device. The average latency for this process was 14.72 seconds. Compared with the virtual geographical scenes constructed by traditional methods, the experiments showed the proposed method advantageous in comprehensiveness, timeliness, and photorealism and abilities to enhance the user's geographical scene perception. [ABSTRACT FROM AUTHOR]

This article discusses children's rights as social semantics, approaching them as a form of self‐description of a paradoxical relationship that has emerged from the late twentieth century within several social systems, a relationship between generational order and children's position as holders of human rights. Charles Taylor's theory on the evolution of the semantics of human value is combined with a wide interdisciplinary array of contributions from Childhood Studies, Social Work, Pedagogy, Studies on Constitutionalism to propose an innovative social ontology of children's rights. Although the UNCRC has been the object of critical scrutiny since the early 1990s, the authors are not aware of any previous attempt to approach children's rights as social semantics in an attempt to illuminate the dynamic and paradoxical coupling within discourses on childhood between a fundamental social process, the reproduction of generational order and a fundamental social institution, human rights as codified in western modernity. The authors argue that while describing a paradoxical coexistence between intergenerational order and human rights, the semantics of children's rights maintains its unity as a cultural form because another semantic distinction, between human rights and personal rights continue to generate social meaning. It is hoped that the scholarly debate will benefit from the contribution of this article to enrich the debate around the social ontology of childhood and children's rights. [ABSTRACT FROM AUTHOR]

Older adults are not actively participating in outdoor physical activities. An understanding of what factors influence older adults' outdoor physical activities is important for informing interventions. This study aims to examine the potential effect of service ontology on the intention of outdoor physical activities in older adults. Face-to-face surveys were conducted with 421 older adults from eight districts in Nanjing, China. One-way analysis of variance and ordinal logistic regression analysis revealed that seven of the 20 factors have a positive effect on intention of outdoor physical activities, including gender, health self-assessment, family structure, accessibility, exercise instructors, emergency service, and staff in outdoor space. To further improve the intention of outdoor physical activities, governments and service designers should focus on providing age-friendly community infrastructures. Moreover, specific attention should be paid toward older females and people with low-income/poor health conditions, and strategies should also be adopted to significantly improve the quantity and instruction efficiency of exercise instructors. [ABSTRACT FROM AUTHOR]

Background. Heart failure represents the terminal stage of various cardiovascular diseases. This study aims to explore the pharmacological mechanisms underlying the protective effect of Ginsenoside Rg3 against heart failure. Methods. Potential targets of Ginsenoside Rg3 were identified using SwissTargetPrediction and the Comparative Toxicogenomics Database, while heart failure‐related genes were retrieved from the Comparative Toxicogenomics Database, Therapeutic Target Database, DisGeNET, and PharmGKB. Overlapping of Ginsenoside Rg3 targets with heart failure‐related genes identified drug‐disease interaction genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the drug‐disease interaction genes to elucidate their biological functions. A protein‐protein interaction network was constructed using the drug‐disease interaction genes, and the hub genes were identified by topological analysis. Additionally, we validate the expression of IL‐6 and TNF by real‐time PCR. Results. The intersection of Ginsenoside Rg3 targets and heart failure‐related genes yielded 15 drug‐disease interaction genes. Enrichment analysis highlighted the involvement of inflammation‐related GO terms and KEGG pathways, such as positive regulation of interleukin‐8 and ‐6 production, regulation of immune effector process, cytokine receptor binding, cytokine activity, adipocytokine signaling pathway, and IL‐17 signaling pathway, which are implicated in the cardioprotective effect. Topological analysis revealed four hub genes: STAT3, CASP3, TNF, and IL-6. The application of Ginsenoside Rg3 significantly reversed the elevated levels of IL‐6 and TNF in the isoproterenol‐treated H9c2 cell line. Conclusions. Our findings suggest that the cardioprotective effect of Ginsenoside Rg3 may be mediated through its anti‐inflammation properties. Further research is required to elucidate and validate the detailed cardioprotective mechanisms of Ginsenoside Rg3. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the anti-tumor effector of Liuwei Dihuang Decoction (LWDHD) in prostate cancer (PCa) and explore the potential mechanism using experimental validation, network pharmacology, bioinformatics analysis, and molecular docking. Methods: CCK test, Clone formation assay and wound-healing assays were used to determine the effect of LWDHD on prostate cancer growth and metastasis. The active ingredients and targets of LWDHD were obtained from the TCMSP database, and the relevant targets were selected by GeneCards, OMIM and DisGeNET databases for PCa. The cross-targets of drugs and disease were imported into the STRING database to construct protein interactions. The network was also visualized using Cytoscape software and core targets are screened using the Network Analyzer plug-in. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using R software. TCGA database was used to analyze the correlation of bioinformatics genes. AutoDock vina was used to predict the molecular docking and binding ability of active ingredients to key targets. Through WB and q-PCR experiments, the above gene targets were detected to verify the effect of LWDHD on PCa. Results: CCK and scratch tests confirmed that LWDHD could inhibit the proliferation, invasion and migration of prostate cancer cells. Clone formation experiments showed that LWDHD inhibited the long-term proliferative capacity of PC3 cells. LWDHD and PCa had a total of 99 common targets, establishing a "drug-ingredient-common target" network. Through GO and KEGG enrichment analysis, PI3K/AKT, MAPK, TP53 pathway, MYC, TNF pathway and other signaling pathways were found. Bioinformatics analysis showed that MYC gene was highly expressed and CCND1 and MAPK1 were low expressed in prostate cancer tissues. In addition, TP53, AKT1, MYC, TNF and CCND1 were positively correlated with MAPK1, among which AKT1 and CCND1 were most closely correlated with MAPK1. Molecular docking results showed that quercetin, kaempferol, β-sitosterol and other main active ingredients of LWDHD treatment for PCa were combined with core proteins MAPK1 and AKT1 well. WB and q-PCR results showed that LWDHD inhibited the expression of PI3K and AKT in PC3 cells. Conclusion: The mechanism of LWDHD therapy for PCa is a multi-target and multi-pathway complex process, which may be related to the biological processes mediated by MAPK1 and AKT1 pathways, such as cell proliferation and inhibition of metastasis, and the regulation of signaling pathways. The PI3K/AKT signaling pathway may be a central pathway of LWDHD to inhibit prostate cancer proliferation. [ABSTRACT FROM AUTHOR]

Autonomous robots are already present in a variety of domains performing complex tasks. Their deployment in open-ended environments offers endless possibilities. However, there are still risks due to unresolved issues in dependability and trust. Knowledge representation and reasoning provide tools for handling explicit information, endowing systems with a deeper understanding of the situations they face. This article explores the use of declarative knowledge for autonomous robots to represent and reason about their environment, their designs, and the complex missions they accomplish. This information can be exploited at runtime by the robots themselves to adapt their structure or re-plan their actions to finish their mission goals, even in the presence of unexpected events. The primary focus of this article is to provide an overview of popular and recent research that uses knowledge-based approaches to increase robot autonomy. Specifically, the ontologies surveyed are related to the selection and arrangement of actions, representing concepts such as autonomy, planning, or behavior. Additionally, they may be related to overcoming contingencies with concepts such as fault or adapt. A systematic exploration is carried out to analyze the use of ontologies in autonomous robots, with the objective of facilitating the development of complex missions. Special attention is dedicated to examining how ontologies are leveraged in real time to ensure the successful completion of missions while aligning with user and owner expectations. The motivation of this analysis is to examine the potential of knowledge-driven approaches as a means to improve flexibility, explainability, and efficacy in autonomous robotic systems. [ABSTRACT FROM AUTHOR]

The illicit traffic of cultural goods remains a persistent global challenge, despite the proliferation of comprehensive legislative frameworks developed to address and prevent cultural property crimes. Online platforms, especially social media and e-commerce, have facilitated illegal trade and pose significant challenges for law enforcement agencies. To address this issue, the European project SIGNIFICANCE was born, with the aim of combating illicit traffic of Cultural Heritage (CH) goods. This paper presents the outcomes of the project, introducing a user-friendly platform that employs Artificial Intelligence (AI) and Deep learning (DL) to prevent and combat illicit activities. The platform enables authorities to identify, track, and block illegal activities in the online domain, thereby aiding successful prosecutions of criminal networks. Moreover, it incorporates an ontology-based approach, providing comprehensive information on the cultural significance, provenance, and legal status of identified artefacts. This enables users to access valuable contextual information during the scraping and classification phases, facilitating informed decision-making and targeted actions. To accomplish these objectives, computationally intensive tasks are executed on the HPC CyClone infrastructure, optimizing computing resources, time, and cost efficiency. Notably, the infrastructure supports algorithm modelling and training, as well as web, dark web and social media scraping and data classification. Preliminary results indicate a 10–15% increase in the identification of illicit artifacts, demonstrating the platform's effectiveness in enhancing law enforcement capabilities. [ABSTRACT FROM AUTHOR]

Classifying domain entities into their respective top-level ontology concepts is a complex problem that typically demands manual analysis and deep expertise in the domain of interest and ontology engineering. Using an efficient approach to classify domain entities enhances data integration, interoperability, and the semantic clarity of ontologies, which are crucial for structured knowledge representation and modeling. Based on this, our main motivation is to help an ontology engineer with an automated approach to classify domain entities into top-level ontology concepts using informal definitions of these domain entities during the ontology development process. In this context, we hypothesize that the informal definitions encapsulate semantic information crucial for associating domain entities with specific top-level ontology concepts. Our approach leverages state-of-the-art language models to explore our hypothesis across multiple languages and informal definitions from different knowledge resources. In order to evaluate our proposal, we extracted multi-label datasets from the alignment of the OntoWordNet ontology and the BabelNet semantic network, covering the entire structure of the Dolce-Lite-Plus top-level ontology from most generic to most specific concepts. These datasets contain several different textual representation approaches of domain entities, including terms, example sentences, and informal definitions. Our experiments conducted 3 study cases, investigating the effectiveness of our proposal across different textual representation approaches, languages, and knowledge resources. We demonstrate that the best results are achieved using a classification pipeline with a K-Nearest Neighbor (KNN) method to classify the embedding representation of informal definitions from the Mistral large language model. The findings underscore the potential of informal definitions in reflecting top-level ontology concepts and point towards developing automated tools that could significantly aid ontology engineers during the ontology development process. [ABSTRACT FROM AUTHOR]

Additive Manufacturing increasingly becomes a viable alternative to conventional manufacturing technologies. However, the knowledge and awareness to achieve a ready-for-manufacturing and quality-ensured design of additively manufactured products are mostly lacking. Knowledge from the manufacturing process on how certain process variables affect the product requirements must already be considered in product design to assure and potentially enhance the quality a priori, thus saving time and cost-intensive iterations. Therefore, this knowledge must be front-loaded as a basis to suitably adapt product and process design simultaneously. This contribution therefore proposes a framework for the simultaneous product and process design synthesis of existing designs by providing insight knowledge about product and process design specific to Fused Layer Modeling. This is achieved via a consolidated ontology-based knowledge representation and its integration in a design environment. Thus, the aim, in contrast to most existing approaches, is to provide this knowledge on a product- and application-specific basis to parallelise product and process design for non-assembly mechanisms and exploit currently unused potential concerning the interaction between the different domains. This can shorten development times and also compensate for a lack of background knowledge in certain areas. A practical use case highlights the general procedure, benefits, and applicability. [ABSTRACT FROM AUTHOR]

The processes of service discovery and composition are crucial tasks in application development driven by Web Services. However, with RESTful Web Service replacing SOAP-based Web Service as the dominant service-providing approach, the research on service discovery and composition should also shift its focus from SOAP-based Web Service to RESTful Web Service. The unstructured, resource-oriented and unified interface characteristics of RESTful Web Service pose challenges to its discovery and composition process. In this work, a framework for implementing RESTful Web Service discovery and automatic composition based on semantic technology is proposed. Firstly, the framework uses the OpenAPI Specification (OAS), which is extended by resource attributes, as the RESTful Web Service description specification, and then supports semantic-based matching discovery and automatic composition by attaching the concepts of domain ontology to the extended OAS. Secondly, the framework is fully adapted to REST features and provides a method for building service composition dependencies during registration, which is used to generate composition schemes during the service discovery process. Finally, the framework provides a discovery method that can return RESTful Web services to the requester in the form of single-point services or service composition schemes according to the magnitude of the semantic similarity with the requester's requirements. We applied the proposed methods to experiment with RESTful Web services in three different fields, and the results show that the methods effectively calculate the similarity between RESTful single-point Web services or composite Web services and service requests with the support of domain ontology. [ABSTRACT FROM AUTHOR]

Multi-example query over knowledge graphs allows users to provide multiple example fragments to express their intention, overcoming the shortage of single example query asks users to give a specific complete query example. Unfortunately, the existing multi-example query ignores the semantic relevance of entities, and query results are inconsistent in compactness, which causes it difficult for users to check their interesting results. Therefore, we first define a multi-example query over ontology-label knowledge graphs to facilitate users to express their query intentions and improve the semantic relevance of query results. Secondly, we propose a matching pattern to analyze users' query intention to prioritize the more compact query results to improve users' satisfaction. Specifically, we first use the OELIndex to filter the search space and improve query efficiency. Then, we construct a connected matching pattern according to query examples to get a more compact query result. After that, we select the pattern fragments with the smallest amount for priority matching by assessing the number of candidate sets, significantly reducing the isomorphic computations. Finally, extensive experiments are carried out on real data sets to verify the effectiveness and practicability of our proposed method. [ABSTRACT FROM AUTHOR]

I evaluate the conception of direct retrieval as originally formulated in the Self-Memory System model (Conway & Pleydell-Pearce [2000]. The construction of autobiographical memories in the self-memory system. Psychological Review, 107(2), 261–288. ). In the hierarchical memory organisation proposed in the Self-Memory System model, direct retrieval is described as a bottom-up associative process. While its theoretical role within this model is clear, systematic empirical examination of direct retrieval, viewed as a natural and observable phenomenon, has been hampered by inconsistent operationalisations. Here, I suggest that direct retrieval should be treated as a theoretical concept, aiming at explaining the phenomenon of involuntary (spontaneously arising) autobiographical memories. I evaluate predictions derived from the concept of direct retrieval against findings on involuntary autobiographical memories obtained over the past 25 years. Most of these predictions are consistent with the evidence, notably, the enhanced episodic specificity and constructive nature of involuntary autobiographical memories. However, the theory also has critical limitations. It did not predict the frequent occurrence of involuntary recollections in daily life, exceeding the prevalence of voluntary memories. Additionally, it overlooked the early emergence of spontaneously arising event memories in ontogenesis and their presence in other species, such as great apes. Future advancements of the Self-Memory System model should integrate evolutionary perspectives to address these limitations. [ABSTRACT FROM AUTHOR]

Objective: The objective of this commentary is to advocate for a holistic, ontology‐based definition of bruxism. The intention is to synthesise the best aspects of current definitions into a structured ontological model, thereby refining and enhancing a comprehensive understanding of the full spectrum of bruxism. Materials and Methods: The commentary elaborates on the process of integrating these insights into a hierarchical ontology that aligns with ontological principles. Settings and Sample Population: Not directly applicable as this is a commentary. Results/Conclusion: The proposed ontology‐based definition of bruxism aims to clarify communication within the medical community and advance research by enabling a comprehensive ontology‐based classification of bruxism. By aligning with ontological principles, this approach aspires to act as a catalyst for further research, discussion and consensus in the field. [ABSTRACT FROM AUTHOR]

Project-based organizations (PBOs) rely on project management as their core business process. These organizations are characterized by their disaggregated structure and diverse authority distribution, leading to complications in synchronizing projects, governance, and functional divisions. In this regard, there is a need of a tool to manage this complexity. This paper introduces a novel approach – the IModel - for modeling, designing and analyzing PBOs, following the Design Science Research Method, that seeks to is creating an artifact that solves a real-world problem in a specific context. This artifact is developed through a conceptual integration between project management (PM) and enterprise architecture (EA) sources based on ontologies. Ontologies are powerful, machine-readable graph-structured models which enables sophisticated models' reasoning and querying. IModel fuses the organizational domain from the ArchiMate EA language with PM insights from PMBOK 7th edition. The resulting IModel has been submitted to an expert's judgement to assess its intended purposes as well, its potential to be applied in the real-world to support the PBO. [ABSTRACT FROM AUTHOR]

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