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Drawing on an in-depth study of physicians facing the Covid-19 pandemic in Italy in 2020, we advance theory on how professionals in their workplace respond to knowledge disruptions associated with complex societal challenges that undermine the adequacy of their knowledge base to solve professional problems. We show that, in the context of uncertainty generated by the knowledge disruption and the inability to mitigate this uncertainty through typical knowledge-based strategies, professionals experience a trail of negative epistemic emotions. Despite these negative epistemic emotions, and motivated by a heightened sense of moral duty, professionals engage in service-oriented practices of collegial and humanistic work that depart from the knowledge-centric practices of their usual work. We detail how the repeated development of positive moral emotions when performing such practices leads professionals to ultimately consolidate and embed service-oriented practices in their professional work. Our study contributes to the literature on professions and organizations by theorizing the distinctive category of knowledge disruptions of unmitigable uncertainty and by uncovering the microlevel dynamics and mechanisms that sustain professionals' responses. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between local wages and the internal structure of the regional knowledge base. The purpose is to assess if the workers' compensations are related to the peculiarities of the knowledge base of the regions in which they supply their labor services. The test of this hypothesis is based on the assessment of the impact of related vis-à-vis unrelated knowledge variety on cross-regional wage differentials. The empirical analysis is carried out by exploiting patent data and a unique employer–employee administrative dataset. First, using OECD-PATREG data on patent filing, we build information entropy indexes proxying the variety of NUTS 3 regions' knowledge bases, and the decomposition in the related and unrelated component. Second, we assess the impact of these indexes on wages based on administrative data from the Italian National Institute of Social Security. Our results suggest that workers employed in regions with a heterogenous knowledge structure earn positive wage premia, while related variety has a negative effect on compensation levels. [ABSTRACT FROM AUTHOR]

Ambidexterity in a business context has focused primarily on how organizations can strategically coordinate multiple, often competing tasks. Within the marketing function, ambidexterity has centered specifically on how frontline employees can best coordinate sales and service activities manifesting at the frontline. This paper systematically reviews the frontline literature to provide a framework that encompasses and reconciles the many forms of ambidexterity that arise at the frontline interface. This work identifies critical areas for future research to better unify the field, allowing for an inclusive understanding of frontline ambidexterity that encompasses any firm or firm representative involvement in the multiple routine and non-routine demands that emerge within a dynamic frontline interface. Notably, this review suggests a nuanced perspective of frontline ambidexterity, including three distinct forms that are labeled dueling, holistic, and synergistic ambidexterity. Thus, the objectives are to (1) assess the current state of the literature pertaining to a broader understanding of frontline ambidexterity, (2) identify classifications of frontline ambidexterity interactions that allow us to better understand the status of our knowledge base, and (3) critically evaluate this understanding to identify gaps and provide insights regarding extensive directions for future research in this domain. [ABSTRACT FROM AUTHOR]

Knowledge is largely recognized as a key driver of survival and growth of new entrants. Previous literature on the role of pre-entry knowledge in post-entry performance has focused on entrepreneurial and managerial capabilities and education and on knowledge incorporated in material and immaterial resources. In this paper, taking to the firm level the intuition behind the notion of economic and technological complexity of countries, we hypothesize that post-entry performance of new firms may be explained by their knowledge base complexity, which captures the reinforcing mechanism between variety and creation of new valuable knowledge. We provide a measure of firm-level complexity derived from the analysis of textual content of patents and exploit it to examine the empirical relation between pre-entry complexity of knowledge and post-entry growth in a sample of Italian firms entering the market in 2009-2011. Baseline results show a significant and positive association between knowledge complexity and post-entry growth, even after controlling for firm characteristics and year, sector, and region fixed effects. Robustness analysis reveals that this positive effect is more significant over the medium run than in the initial years after entry, while relatively weaker for the so-called Innovative Small and Medium-Sized Enterprises. [ABSTRACT FROM AUTHOR]

We explore the origins and the development of hydraulic fracking technology that made possible United States' unexpected rise as a top producer of oil and gas. As hydraulic fracking was first used extensively to produce shale gas and only later to produce shale oil, we focus on analyzing what is often referred to as the "shale gas revolution." We trace how the shale gas ecosystem evolved, what role different actors played in the unexpected shale gas revolution, and how the strategies and knowledge bases of the different actors differed and evolved over time. [ABSTRACT FROM AUTHOR]

Existing research extensively explores challenges faced by novice teachers with initial teacher education (ITE) backgrounds, whereas those without ITE exposure (non-ITE backgrounds) remain less investigated. Utilising the ecological model of teacher agency, this study examines the affordances and constraints encountered by English as a foreign language (EFL) teachers with non-ITE backgrounds during their first year at 12 Chinese state junior high schools. Thematic analysis of interviews with 21 purposively sampled participants reveals that non-ITE backgrounds pose constraints, such as limited pedagogical knowledge, perceived inadequacy by peers with ITE exposure, and professional identity crises. However, significant affordances were also identified, including broad knowledge bases from diverse academic backgrounds and varied internship experiences, high intrinsic motivation, and robust innovation capabilities. Implications for teacher education programmes and school administration are discussed, highlighting the need for tailored support mechanisms to address the unique challenges and leverage the strengths of teachers without ITE backgrounds. [ABSTRACT FROM AUTHOR]

The rapid growth of biomedical literature makes it challenging for researchers to stay current. Integrating knowledge from various sources is crucial for studying complex biological systems. Traditional text-mining methods often have limited accuracy because they don't capture semantic and contextual nuances. Deep-learning models can be computationally expensive and typically have low interpretability, though efforts in explainable AI aim to mitigate this. Furthermore, transformer-based models have a tendency to produce false or made-up information—a problem known as hallucination—which is especially prevalent in large language models (LLMs). This study proposes a hybrid approach combining text-mining techniques with graph neural networks (GNNs) and fine-tuned large language models (LLMs) to extend biomedical knowledge graphs and interpret predicted edges based on published literature. An LLM is used to validate predictions and provide explanations. Evaluated on a corpus of experimentally confirmed protein interactions, the approach achieved a Matthews correlation coefficient (MCC) of 0.772. Applied to insomnia, the approach identified 25 interactions between 32 human proteins absent in known knowledge bases, including regulatory interactions between MAOA and 5-HT2C, binding between ADAM22 and 14-3-3 proteins, which is implicated in neurological diseases, and a circadian regulatory loop involving RORB and NR1D1. The hybrid GNN-LLM method analyzes biomedical literature efficiency to uncover potential molecular interactions for complex disorders. It can accelerate therapeutic target discovery by focusing expert verification on the most relevant automatically extracted information. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and computer hardware, semantic communication has been attracting great interest. As an emerging communication paradigm, semantic communication can reduce the requirement for channel bandwidth by extracting semantic information. This is an effective method that can be applied to image acquisition of unmanned aerial vehicles, which can transmit high-data-volume images within the constraints of limited available bandwidth. However, the existing semantic communication schemes fail to adequately incorporate the guidance of task requirements into the semantic communication process and are difficult to adapt to the dynamic changes of tasks. A task-oriented image semantic communication scheme driven by knowledge base is proposed, aiming at achieving high compression ratio and high quality image reconstruction, and effectively solving the bandwidth limitation. This scheme segments the input image into several semantic information unit under the guidance of task requirements by Yolo-World and Segment Anything Model. The assigned bandwidth for each unit is according to the task relevance scores, which enables high-quality transmission of task-related information with lower communication overheads. An improved metric weighted learned perceptual image patch similarity (LPIPS) is proposed to evaluate the transmission accuracy of the novel scheme. Experimental results show that our scheme achieves a notable performance improvement on weighted LPIPS while the same compression ratio compared with traditional image compression schemes. Our scheme has a higher target capture ratio than traditional image compression schemes under the task of target detection. [ABSTRACT FROM AUTHOR]

Information on restoration science and practice is dispersed across large numbers of scientific papers, reports, books, and other resources, and there is a lack of synthetic approaches and of linkages between ecological theory and practice. With recent calls for scaling up ecological restoration, there is an urgent need for improving the effectiveness of restoration ecology by presenting existing knowledge in an organized and accessible form. Practitioners benefit from knowing which theories explain patterns and processes in a specific ecosystem, and scientists need an overview of empirical evidence supporting current theories. Strengthening links between restoration practice and science benefits both areas. Based on a new approach used for organizing and assessing hypotheses in invasion biology, we suggest the development of an interactive online platform that promotes the integration of restoration science and practice by (1) presenting an overview of restoration ecology; (2) mapping theoretical work relevant for ecological restoration; (3) displaying direct links to relevant publications; and (4) providing summaries of empirical evidence for ecological theories in specific settings. This online knowledge base should be developed in an open process, bringing together the restoration community with experts in semantic web and natural language processing, library scientists, web designers, and other specialists. The platform should become an evolving, searchable, openly accessible, and intuitively organized tool for future ecological restoration. [ABSTRACT FROM AUTHOR]

This study investigates the impact of European Key Enabling Technologies (KETs) on regional innovation. KETs are a group of six technologies that share features with General-Purpose Technologies and are anticipated to contribute to innovation, Europe’s industrial competitiveness, and tackling grand societal challenges. As previous studies show innovation-driving effects of KETs, we are interested in the role of KETs in the regional technological space and its effect on innovation within a region. We analyse data from 141 German Labor Market Regions (LMRs) over a 15-year period and find varying and even negative impacts of individual KETs on regional knowledge creation. These findings emphasize KETs’ heterogeneity and the need for tailored regional policies. [ABSTRACT FROM AUTHOR]

This article analyses the novel form of live political fact-checking, as performed by the Norwegian fact-checking organisation Faktisk.no during the Norwegian parliamentary election campaign in 2021. The aim of the study was to investigate the epistemological consequences of introducing a breaking news logic to political fact-checking. Through methods of participatory observation, interviews and textual analysis, the study finds that Faktisk.no used several strategies to bridge the 'epistemic gap' between the logics of breaking news and political fact-checking. Combined, these strategies pushed the live fact-checking towards a confirmative epistemology, implying that the live political fact-checking confirmed (1) knowledge already believed to be true and (2) hegemonic perspectives on what constitutes important and reliable information. The findings thereby point to a potential reorientation of political fact-checking from being a critical corrective of political elites to confirming the perspectives and knowledge base of the same elites. [ABSTRACT FROM AUTHOR]

The underlying mechanism and boundary condition of the effects of emerging digital technology on firms' innovation activities need to be analysed. This study intends to explore how digital technology adoption (DTA) influences innovation performance (IP) in the context of firm digital transformation based on digital empowerment perspective and knowledge-based view. The mediating effect of knowledge base breadth (KBB) and knowledge base depth (KBD) and the moderating effect of technological capability (TC) are proposed and evaluated using panel data from Chinese listed companies. The results indicate that DTA significantly improves innovation performance, and this effect is mediated by KBB and KBD. Moreover, the indirect effect of the KBB is moderated by TC, which is not significant for KBD. Our findings offer an enhanced understanding of the performance implications of digital technologies and provide managerial insights into how unique competitive advantages and sustainable development of firms can be maintained in the digital economy era. [ABSTRACT FROM AUTHOR]

Common reductionist contractions in thinking or writing about personality and individual differences are to conflate personality, dispositional traits, the Big Five, and self-reports. To avoid conceptual confusions and communicate more effectively, we should bear in mind: (a) Personality is much more than dispositional traits or basic tendencies, (b) basic tendencies are more than just Big Five traits, and (c) self-reports of traits—which capture explicit self-concepts—are just one out of many approaches to trait measurement. These distinctions should be reflected in our thinking, writing, and communication. Attending to them can help clarify what has been studied and (re-)contextualize our knowledge bases. Personality psychology is a science, and scientific language must be accurate, precise, and nuanced. Plain language summary: Personality, dispositional traits, the Big Five, and self-reports are often mixed up. To avoid confusions and communicate more effectively, we should bear in mind: (a) Personality is much more than traits, (b) traits are more than just the Big Five, and (c) self-reports of traits—which capture self-concepts—are just one out of many approaches to measuring traits. These distinctions should be reflected in thinking, writing, and communication. Attending to them can help clarify what has been studied and what we know. Personality psychology is a science, and scientific language must be accurate, precise, and nuanced. [ABSTRACT FROM AUTHOR]

Deepfake detection has become increasingly important in recent years owing to the widespread availability of deepfake generation technologies. Existing deepfake detection methods present two primary limitations; i.e., they are trained on a specific type of deepfake dataset, which renders them vulnerable to unseen deepfakes, and they regard deepfakes as a "black box" with limited explainability, making it difficult for non-AI experts to understand and trust the decisions. Hence, this article proposes a novel neurosymbolic deepfake detection framework that exploits the fact that human emotions cannot be imitated easily owing to their complex nature. We argue that deep fakes typically exhibit inter- or intra-modality inconsistencies in the emotional expressions of the person being manipulated. Thus, the proposed framework performs inter- and intra-modality reasoning on emotions extracted from audio and visual modalities using a psychological and arousal-valence model for deepfake detection. In addition to fake detection, the proposed framework provides textual explanations for its decisions. The results obtained using the Presidential Deepfakes Dataset and World Leaders Dataset of real and manipulated videos demonstrate the effectiveness of our approach in detecting deepfakes and highlight the potential of a neurosymbolic approach for expandability. [ABSTRACT FROM AUTHOR]

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Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, particularly in the construction of the intelligent question-answering system. These systems, especially in specialized fields, usually rely on NLP through the retrieval of corpus and answering databases to efficiently provide accurate and concise answers. This paper focuses on the national confidentiality publicity and education field, aiming to address the dilemma of inaccurate knowledge retrieval in this field. Therefore, we design an intelligent confidentiality question-answering system CPEQA by comprehensively utilizing the LLMs platform and information retrieval technique. CPEQA is capable of providing professional answers to questions about Chinese confidentiality publicity and education raised by users. Additionally, we also integrate the conventional database retrieval technique and LLMs into the database query construction, enabling CPEQA to perform real-time queries and data analysis for both single-table and multi-table querying tasks. Through extensive experiments with generated query sentences, we show both methodological comparisons and empirical evaluations of CPEQA's performance. Experimental results indicate that CPEQA has achieved competitive results on answering precision, recall rate and other metrics. Finally, we explore the challenges of the CPEQA system associated with these techniques and outline potential avenues for future research in this emerging field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Small Business & Entrepreneurship is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Innovation usually requires time‐consuming exploratory approaches. However, external shocks and related crises, such as the COVID‐19 pandemic, lead to severe time pressures, which require short‐term R&D results. We investigate how organizations' prior collaboration and existing knowledge not only helped them cope with the crisis but also affected the vaccine's development performance. Specifically, we investigate the R&D outcomes of 386 organizations involved in the COVID‐19 vaccine's development within the first 18 months after the pandemic's outbreak. The results reveal that under urgency, organizations with prior scientific collaborations and technological knowledge exhibit a higher R&D performance. Furthermore, a broad network of diverse collaborators strengthened this relationship, thereby calling for more interdisciplinary R&D activities. We therefore extend the literature on innovation speed and strengthen long‐term R&D outcomes' role in organizations with a broad existing knowledge base and collaboration networks. We do so by specifically supporting such organizations' ability to integrate their previous R&D's and collaborations' knowledge to achieve rapid innovative outcomes under urgency. [ABSTRACT FROM AUTHOR]

Poetry was a unique literary genre in ancient China as an important way to express sentiments. Chinese ancient poetry not only has simple words, strict meters and rich semantic relationships, but also widely uses rhetorical techniques such as simile and personification, as well as metaphorical means such as allusion and imagery, which makes it difficult to understand their implicit sentiments quickly and accurately. Therefore, this paper attempts to make full use of the semantic features of Chinese ancient poetry text and the knowledge feature of related domains under the guidance of the research paradigm of digital intelligence integration, and based on which, proposed Sentiment Analysis Model of Chinese Ancient Poetry based on Multidimensional Attention (SAMCAP). This model extracts semantic features from Chinese ancient poetry text, meanwhile designs a multidimensional attention to extract the knowledge feature from the knowledge base of Chinese ancient poetry. The recognition of Chinese ancient poetry sentiment is performed by integrating the textual feature and knowledge feature. Comparison experiments on the open ancient poetry corpus verified the effectiveness of the proposed model, and the ablation experiment explored the importance of different knowledge to the sentiment analysis result of Chinese ancient poetry. [ABSTRACT FROM AUTHOR]

Introduction: Clinical decision‐making in oncology is a complex process, with the primary goal of identifying the most effective treatment tailored to individual cancer patients. Many factors influence the treatment decision: disease‐ and patient‐specific criteria, the increasingly complex treatment landscape, market authorization and drug availability, financial aspects, and personal treatment expertise. In the domain of genitourinary cancers, particularly prostate cancer, decision‐making is challenging. Despite the prevalence of this malignancy, there are few in‐depth explorations of these factors within real‐world scenarios. Understanding and refining this intricate decision‐making process is essential for future successful clinical decisions and the integration of computerized decision support into clinicians' workflows. Aim: The objective of this study is to improve the current knowledge base and evidence of the factors that influence treatment decision‐making for patients with genitourinary cancers. Methods: Assessment of how routine treatment decisions are made for genitourinary cancers was performed by a mixed‐methods study, encompassing field observations and focus group discussions. Results: In total, we identified 59 factors that influence clinical decision‐making in oncology, specifically for genitourinary and prostate cancer. Of these, 23 criteria can be classified as decision‐maker‐related criteria encompassing personal, cognitive, and emotional attributes and factors of both, healthcare professionals and patients. Moreover, 20 decision‐specific criteria have been identified that refer to clinical and disease‐related factors, followed by 16 contextual decision factors that describe the relevant criteria introduced by the specific circumstances and environment in which the treatment decision is made. Conclusion: By presenting an exhaustive set of decision factors and providing specific examples for genitourinary cancers, this observational study establishes a possible framework for a better understanding of decision‐making. Moreover, we specify and expand the set of decision factors, while emphasizing the importance of cognitive, emotional, and human factors, as well as the quality and accessibility of decision‐relevant information. [ABSTRACT FROM AUTHOR]

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper. [ABSTRACT FROM AUTHOR]

A stock watering place (SWP) is a type of travelling stock reserve, and Western Local Land Services (WLLS) is the controlling authority of SWPs in the semi-arid Western region of New South Wales, Australia. Over half SWPs have ground tanks that were installed in the 1800s to provide water for travelling stock. The location of many SWP ground tanks is thought to have been influenced by traditional Aboriginal knowledge, however, their position for capturing contemporary surface flows has not previously been assessed. This study aims to use multicriteria analysis within a Geographic Information System (GIS) to investigate whether SWP ground tanks are at the most suitable rainwater harvesting (RWH) location. ArcGIS was used to analyse data to identify RWH suitability based on slope, soil group, and drainage density. These layers were then weighted and combined to produce a final RWH suitability map. Results show that most (50.7%) ground tanks are in high RWH suitability locations, 47.9% are in medium RWH suitability, 1.4% are in low suitability, and none are in unsuitable locations. These results indicate that 1800s RWH suitability selection methods were highly effective. The findings of this study will contribute to the WLLS SWP knowledge base and will assist in prioritising SWPs for works to improve RWH efficiency, thereby ensuring reliable stock water on SWPs. [ABSTRACT FROM AUTHOR]

People use search engines for various topics and items, from daily essentials to more aspirational and specialized objects. Therefore, search engines have taken over as people's preferred resource. The "How To" prefix has become familiar and widely used in various search styles to find solutions to particular problems. This search allows people to find sequential instructions by providing detailed guidelines to accomplish specific tasks. Categorizing instructional text is also essential for task-oriented learning and creating knowledge bases. This study uses the "How To" articles to determine the multi-label instruction category. We have brought this work with a dataset comprising 11,121 observations from wikiHow, where each record has multiple categories. To find out the multi-label category meticulously, we employ some transformer-based deep neural architectures, such as Generalized Autoregressive Pretraining for Language Understanding (XLNet), Bidirectional Encoder Representation from Transformers (BERT), etc. In our multi-label instruction classification process, we have reckoned our proposed architectures using accuracy and macro f1-score as the performance metrics. This thorough evaluation showed us much about our strategy's strengths and drawbacks. Specifically, our implementation of the XLNet architecture has demonstrated unprecedented performance, achieving an accuracy of 97.30% and micro and macro average scores of 89.02% and 93%, a noteworthy accomplishment in multi-label classification. This high level of accuracy and macro average score is a testament to the effectiveness of the XLNet architecture in our proposed 'InstructNet' approach. By employing a multi-level strategy in our evaluation process, we have gained a more comprehensive knowledge of the effectiveness of our proposed architectures and identified areas for forthcoming improvement and refinement. [ABSTRACT FROM AUTHOR]

The construction of questions is an essential component in educational assessment and student learning processes. However, manually constructing questions is a complex task that requires not only professional training, substantial experience, and extensive resources from teachers but is also time-consuming. This article introduces an Automatic Question Generation (AQG) technology based on a prompt pattern to alleviate this burden and address the ongoing need for new questions in education. The essence of this method lies in constructing a prompt pattern grounded on a collective knowledge base derived from teachers, thereby enhancing the quality of the questions produced. Practical applications and expert evaluations demonstrate that integrating a prompt pattern with a collective knowledge base into Large Language Models (LLMs) results in high-quality questions with statistically significant results. These questions not only meet educational standards but also approach the quality of manually constructed questions by teachers in certain aspects. Our research further emphasizes the feasibility of AI-teacher collaboration in education. [ABSTRACT FROM AUTHOR]

Short text classification has been widely used in many fields. Due to the scarcity of labelled data, implementing short text classification under semi‐supervised learning setting has become increasingly popular. Semi‐supervised short text classification methods based on graph neural networks can achieve state‐of‐the‐art classification performance by utilizing the expressive power of graph neural networks. However, these methods usually fail to mine the hidden patterns of a large amount of short text node data in the graph to optimize the short text node embeddings, which limits the semantic representation power of the short texts, thus leading to suboptimal classification performance. To overcome the limitation, this paper proposes a novel semi‐supervised short text classification method called the Heterogeneous Graph Contrastive Learning with Adaptive Data Augmentation (HGCLADA). In the knowledge bases guided soft prompt‐based data augmentation component, the related words of the tag words are used to optimize the soft prompts for generating diverse augmented samples. In the heterogeneous graph contrastive learning framework component, a heterogeneous graph that is constructed using short texts and keywords and an effective edge augmentation scheme based on a short text clustering algorithm are proposed. The optimized short text embeddings can be obtained to achieve the effective semi‐supervised short text classification. Extensive experiments on six benchmark datasets show that our HGCLADA method outperforms four classes of state‐of‐the‐art methods in terms of classification accuracy, especially with significant performance improvements of 8.74% on the TagMyNews dataset when each class only contains 20 labelled data. [ABSTRACT FROM AUTHOR]

Background Aim Method Results Conclusion Effective eating disorder (ED) treatments are drastically needed for patients with experiences of sustained and prolonged emotional, physical and sexual childhood maltreatment (CM) that often results in post‐traumatic sequelae such as severe, complex posttraumatic stress disorder (cPTSD). EDs with comorbid cPTSD (cPTSD‐ED) have protracted treatment courses and poorer prognoses.To summarise the knowledge base on cPTSD‐ED with specific emphasis on disturbances in self‐organisation (DSO) in relation to therapeutic alliance (TA) processes.Expert opinions based on current relevant literature are critically examined.Preliminary insights on change and alliance processes suggest that neglecting to address emotional‐relational processes in the conceptualisation and treatment of cPTSD‐ED impedes treatment progress.We hypothesise that the DSO construct inherent in cPTSD‐ED calls for a focus on TA processes in addition to traditional ED treatment elements such as nutritional rehabilitation and behaviour change, and regardless of ED treatment provided. More process research on a larger scale is urgently called for. [ABSTRACT FROM AUTHOR]

Most of the present researches on wise medical incorporate a static supporting data, while medical knowledge and rules are featured with a dynamic change, which affect calculation efficiency and accuracy of the result. This paper combined data mining technology with expert system framework, established a medical expert system with a dynamic knowledge base. In the innovative dynamic system, samples were together with their classification results would be added to the knowledge base. In addition, privacy protection was considered in the expert system by means of data removal and conversion of attribute value. At last, for a comprehensive and precise evaluation of both numeric and character attribute data, we proposed a new similarity measurement algorithm termed as E–JD, which combinate Euclidean Distance algorithm with Jaccard Distance algorithm. For more improvement of the data classifasion accuracy, based on KNN and the dynamic knowledge base, the innovative algorithm of D-SNN was proposed, with a modification in the selection and decision of the nearest neighbor objects. The experimental comparison between 1NN, KNN, KNN-means and D-SNN algorithm, shows that the classification performance of the proposed algorithm, D-SNN, exhibits an impressive improvement. [ABSTRACT FROM AUTHOR]

Motivation Feature selection approaches are widely used in gene expression data analysis to identify the most relevant features and boost performance in regression and classification tasks. However, such algorithms solely consider each feature's quantitative contribution to the task, possibly limiting the biological interpretability of the results. Feature-related prior knowledge, such as functional annotations and pathways information, can be incorporated into feature selection algorithms to potentially improve model performance and interpretability. Results We propose an embedded integrative approach to feature selection that combines weighted LASSO feature selection and prior biological knowledge in a single step, by means of a novel score of biological relevance that summarizes information extracted from popular biological knowledge bases. Findings from the performed experiments indicate that our proposed approach is able to identify the most predictive genes while simultaneously enhancing the biological interpretability of the results compared to the standard LASSO regularized model. Availability and implementation Code is available at https://github.com/DEIB-GECO/GIS-weigthed_LASSO. [ABSTRACT FROM AUTHOR]

Motivation With the exponential growth of the life sciences literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. The identification of entities in texts, such as diseases or genes, and their normalization, i.e. grounding them in knowledge base, are crucial steps in any BTM pipeline to enable information aggregation from multiple documents. However, tools for these two steps are rarely applied in the same context in which they were developed. Instead, they are applied "in the wild," i.e. on application-dependent text collections from moderately to extremely different from those used for training, varying, e.g. in focus, genre or text type. This raises the question whether the reported performance, usually obtained by training and evaluating on different partitions of the same corpus, can be trusted for downstream applications. Results Here, we report on the results of a carefully designed cross-corpus benchmark for entity recognition and normalization, where tools were applied systematically to corpora not used during their training. Based on a survey of 28 published systems, we selected five, based on predefined criteria like feature richness and availability, for an in-depth analysis on three publicly available corpora covering four entity types. Our results present a mixed picture and show that cross-corpus performance is significantly lower than the in-corpus performance. HunFlair2, the redesigned and extended successor of the HunFlair tool, showed the best performance on average, being closely followed by PubTator Central. Our results indicate that users of BTM tools should expect a lower performance than the original published one when applying tools in "the wild" and show that further research is necessary for more robust BTM tools. Availability and implementation All our models are integrated into the Natural Language Processing (NLP) framework flair: https://github.com/flairNLP/flair. Code to reproduce our results is available at: https://github.com/hu-ner/hunflair2-experiments. [ABSTRACT FROM AUTHOR]

This study addresses the characteristics of rock masses in gold mine engineering, analyzing 7 key factors affecting rock mass stability:rock uniaxial compressive strength, RQD value, joint structural face conditions, joint structural face spacing, groundwater conditions, effect of joint structural face orientation on engineering, and on-situ stress value. These 7 indicators were adjusted to establish the rock mass quality M-RMR safety evaluation system. Using the M-RMR system, the engineering rock mass quality grades were classified for the Jiaojia Gold Mine-s directly managed mining area, Sizhuang mining area, and Wang-ershan mining area. Furthermore, a neural network knowledge base model was developed to correlate the underground rock mass quality at Jiaojia Gold Mine with its influencing factors, achieving intelligent grading of the rock mass quality for engineering purposes. To simplify the M-RMR indicator system for easier practical application, variable importance projection (VIP) was used to rank the information carried by the 7 indicators, allowing the removal of unimportant variables one by one. The simplified prediction model was built using partial least squares regression of single dependent variables (PLS1). This simplified model can accurately grade rock mass quality using fewer evaluation indicators, demonstrating practical application value. [ABSTRACT FROM AUTHOR]

This study investigated the development of divergent thinking (DT) in early childhood. We followed 107 4-year-olds for 1.5 years. Children's DT was assessed with the Alternative Uses Task (AUT) every 6 months, four times in total. Within the AUT, children were asked to generate unusual uses of common objects while explaining how they came up with these uses. Based on the generated uses, two product measures were computed: fluency and originality. We also applied process measures to gain insight into the thinking processes children used to come up with uses. Based on the coded data of children's verbal explanations of how they generated uses, two process measures were computed: memory retrieval (retrieving uses from episodic and semantic long-term memory) and mental operations (performing mental operations on the object to generate a use). The results revealed substantial growth in fluency and originality with age. The frequencies of occurrence of both memory retrieval and mental operations also increased with age. In line with previous findings, this longitudinal study shows that creativity develops throughout early childhood. Moreover, this study adds to the current knowledge base by providing researchers a first glance at the development of thinking processes underlying DT in this age range. [ABSTRACT FROM AUTHOR]

Scientists are increasingly specializing to manage the vast knowledge bases within their domains, making knowledge-sharing strategies essential for scientific progress. Among these strategies, international scientific collaboration has increased markedly. We analyse the role of thematic specialization as one of its drivers in the context of Chagas research, a neglected disease. Since there is a strong research tradition in Latin America on this topic, it is an interesting context to study centre-periphery research dynamics. Using a novel bibliometric database on Chagas research during 1990–2019, we exploit its panel structure in a gravity-type econometric model. The findings show that the degree of specialization and its proximity among partners boost joint publications. Both effects are particularly important for country dyads involving authors from the periphery linked to authors in the centre. This paper is the first empirical study to assess the role of specialization as a driver of international scientific collaboration. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate detection of wildlife, particularly small and hidden animals, is crucial for conservation efforts. Traditional image-based methods often struggle in complex environments. This study introduces a novel approach that combines image and text data to improve detection accuracy. By incorporating textual information about animal characteristics and leveraging a Concept Enhancement Module (CEM), our model can better understand and locate animals, even in challenging conditions. Experimental results demonstrate a significant improvement in detection accuracy, achieving an average precision of 95.8% on a challenging wildlife dataset. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This represents a substantial improvement compared to existing state-of-the-art methods. Our multimodal approach offers a promising solution for enhancing wildlife monitoring and conservation efforts. Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field. [ABSTRACT FROM AUTHOR]

Large language models (such as GPT) exhibit instability and inauthenticity in professional domain Q&A applications. To address this issue, this paper proposed a method to enhance intent recognition by domain knowledge (EIRDK) for large language models. The method involved three specific strategies: a) scoring and filtering the GPT output using a domain knowledge base, b) training the domain knowledge word vector mode to optimize prompt, e) utilizing feedback from GPT to improve the coherence between the domain word vector model and the GPT model. Experimental analysis demonstrates that, compared to the standard GPT model, the new method achieves a 25% improvement in intent understanding accuracy on the private dataset and a 12% increase on the CMID dataset. The results validate the effectiveness of the EIRDK method. [ABSTRACT FROM AUTHOR]

The article features the speakers and subjects that were included in the original panel at American Library Association (ALA) annual conference. These include "Interconnecting Worlds: Weaving Community Narratives, Andean Histories & the Library's Collections," by Giselle M. Avillés, "Whose History: A Culturally Responsive Approach in Public Library Special Collections" by Alex Mada, and "The Midwest Indigenous Cartography Project" by Heather Sloan.

Cognisant of the thinness of the knowledge base on the dynamics of intergenerational leisure involving grandchildren and their grandparents, this transcendental phenomenological study explored the promises of the Grand Travel phenomenon as a new source of informal intergenerational learning (IGL) from the lens of the lived experiences of 15 Filipino grandchildren during family leisure. Guided by Amadeo Giorgis' four-step analysis, the thick and rich narratives shared by the participants afforded the development of a taxonomy of affordances (relational, informational, and inspirational) that typify the intergenerational learning (IGL) emanating from the emotional experiences of grandchildren during Grand Travel. The Seesaw of Emotional Experiences, holds several promises to family leisure scholars and practitioners in expanding the knowledge frontiers and research agenda on intergenerational bonds between the grandparent and grandchild through Grand Travel. [ABSTRACT FROM AUTHOR]

Critical infrastructure mainly performs its role through an industrial control system (ICS). Organizations conduct cyber exercises between red and blue teams, focusing on offense and defense. Practical exercises require explicit attack scenarios and corresponding defense strategies. However, systematic guides for deriving cyberattack scenarios or defense strategies still need to be improved. This paper proposes a guide for establishing realistic attack scenarios and defense strategies for cybersecurity exercises in ICS environments. Attack scenario generation is divided into four steps: generating attack references, deriving attack sequences, mapping threat information, and mapping vulnerable implementation patterns. Deriving a defensive strategy consists of two steps parallel to developing an attack scenario: deriving containment and eradication. The methodology we propose guides exercise planning based on a knowledge base, thereby assisting exercise planners in generating various scenarios and deriving clear defense strategies. We showed that a clear exercise plan could be established through a case study. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Potato is the fourth largest food crop in the world, but potato cultivation faces serious threats from various diseases and pests. Despite significant advancements in research on potato disease resistance, these findings are scattered across numerous publications. For researchers, obtaining relevant knowledge by reading and organizing a large body of literature is a time-consuming and labor-intensive process. Therefore, systematically extracting and organizing the relationships between potato genes and diseases from the literature to establish a potato gene-disease knowledge base is particularly important. Unfortunately, there is currently no such gene-disease knowledge base available. Methods: In this study, we constructed a Potato Gene-Disease Knowledge Base (PotatoG-DKB) using natural language processing techniques and large language models. We used PubMed as the data source and obtained 2,906 article abstracts related to potato biology, extracted entities and relationships between potato genes and related disease, and stored them in a Neo4j database. Using web technology, we also constructed the Potato Gene-Disease Knowledge Portal (PotatoG-DKP), an interactive visualization platform. Results: PotatoG-DKB encompasses 22 entity types (such as genes, diseases, species, etc.) of 5,206 nodes and 9,443 edges between entities (for example, gene-disease, pathogen-disease, etc.). PotatoG-DKP can intuitively display associative relationships extracted from literature and is a powerful assistant for potato biologists and breeders to understand potato pathogenesis and disease resistance. More details about PotatoG-DKP can be obtained at https://www.potatogd.com.cn/. [ABSTRACT FROM AUTHOR]

The purpose of entity linking is to connect entity mentions in text to real entities in the knowledge base. Existing methods focus on using the text topic, entity type, linking order, and association between entities to obtain the target entities. Although these methods have achieved good results, they ignore the exploration of candidate entities, leading to insufficient semantic information among entities. In addition, the implicit relationship and discrimination within the candidate entities also affect the accuracy of entity linking. To address these problems, we introduce information about candidate entities from Wikipedia and construct a graph model to capture implicit dependencies between different entity decisions. Specifically, we propose a cascade attention mechanism and develop a novel local entity linkage model termed CAM-LEL. This model leverages the interaction between entity mentions and candidate entities to enhance the semantic representation of entities. Furthermore, a global entity linkage model termed DG-GEL based on a dynamic graph is established to construct an entity association graph, and a random walking algorithm and entity entropy are used to extract the implicit relationships within entities to increase the differentiation between entities. Experimental results and in-depth analyses of multiple datasets show that our model outperforms other state-of-the-art models. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The technology of military equipment entity extraction, a crucial component in constructing military knowledge bases, holds significant research value and theoretical importance for guiding the development and improvement of equipment support forces. In the military domain, equipment entities exhibit a phenomenon of nesting, where one entity is contained within another, and abbreviations or codes are frequently used to represent these entities. To address this complexity, this paper proposes a method named CoTNER for extracting entities. Initially, a large-scale language model is used to perform data augmentation with chain-of-thought on the original dataset, providing additional semantic and contextual information. Subsequently, the augmented dataset is fine-tuned on a small-scale language model to adapt it to the task of military equipment entity extraction and to enhance its ability to learn complex rules specific to the domain of military equipment. Additionally, a high-quality data filtering strategy based on instruction-following difficulty scoring is proposed to address the catastrophic forgetting issue that may occur during the fine-tuning of large language models. The experimental results demonstrate that the proposed military equipment entity extraction method outperforms mainstream traditional deep learning methods, validating the effectiveness of CoTNER. [ABSTRACT FROM AUTHOR]

This paper compares two approaches to support Business Process Analysis (BPA) for the construction of a Business Process Knowledge Base (BPKB). The methodology is based on the BPA Canvas metamodel, which starts from the preliminary output of the business analysis (an interview). We focus on the extraction of key elements from the text to build the BPKBcore. We experiment with approaches based on Natural Language Processing (NLP) and Large Language Models (LLMs), using a running example to compare the outcomes with manual annotations. The experiment shows that the LLM-based approach yields better performance, especially with enriched prompts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A question-answering (QA) system is a key application in the field of natural language processing (NLP) that provides relevant answers to user queries written in natural language. In factoid QA using knowledge bases, predicting the semantic category of answers, such as location, person, or numerical values, helps to reduce the search spaces and is an essential step in formal query construction for answer retrieval. However, finding the semantics in sequence data like questions is challenging. In this regard, Recursive neural networks based deep learning methods have been applied. But, they are inefficient in handling long-term dependencies. Recently, pre-trained language models employing transformers are proven effective and can generate context-dependent embedding for words and sentences from their encoders with attention mechanisms. However, to train an efficient transformer model for semantic category prediction requires a large dataset and high computational resources. Therefore, in this work, we employ a transfer learning approach using pre-trained transformer models by efficiently adapting them to predict the semantic category of answers from input questions. Here, embeddings from the encoders of the text-to-text transfer transformer (T5) model have been leveraged to obtain an efficient question representation and to train the classification model which is named as QcT5. Along with QcT5, an extensive experimental study on other recent transformer models - BERT, RoBERTa, DeBERTa, and XLNet, is conducted, and their performance is analyzed in various fine-tuning settings. Experimental results indicate that the QcT5 model significantly improves the performance compared to the selected state-of-the-art methods by achieving an f1-score of 98.7%, 89.9% on TREC-6, and TREC-50 datasets respectively. [ABSTRACT FROM AUTHOR]

AbstractThis essay was invited upon the receipt of the American Psychological Association Division 15’s Career Achievement Award for Distinguished Psychological Contributions to Education in 2021. I propose that modern education should prepare students to continue learning so they can adapt to changing times, circumstances, and knowledge bases. A key step in adaptation is recognizing that there is something new. I review evidence from three sets of previous studies that show traditional instruction succeeds in helping students complete routine tasks but falls well short of preparing students to learn from new information and adapt it to novel situations. I try to understand these results from three levels of analysis: The overarching goals of instruction; the psychological assumptions underlying the instruction; and the in situ demands and natural responses of classroom learners. Given these analyses, I motivate four instructional design principles that emphasize helping students innovate new ideas and solutions, which in turn, leads them to focus on new information and prepares them to adapt and learn from future situations. [ABSTRACT FROM AUTHOR]

Network security policy is regarded as a guideline for the use and management of the network environment, which usually formulates various requirements in the form of natural language. It can help network managers conduct standardized network attack detection and situation awareness analysis in the overall time and space environment of network security. However, in most cases, due to configuration updates or policy conflicts, there are often differences between the real network environment and network security policies. In this case, the consistency detection of network security policies is necessary. The previous consistency detection methods of security policies have some problems. Firstly, the detection direction is single, only focusing on formal reasoning methods to achieve logical consistency detection and solve problems. Secondly, the detection policy field is not comprehensive, focusing only on a certain type of problem in a certain field. Thirdly, there are numerous forms of data structures used for consistency detection, and it is difficult to unify the structured processing and analysis of rule library carriers and target information carriers. With the development of intelligent graph and data mining technology, the above problems have the possibility of optimization. This article proposes a new consistency detection approach for network security policy, which uses an intelligent graph database as a visual information carrier, which can widely connect detection information and achieve comprehensive detection across knowledge domains, physical devices, and detection methods. At the same time, it can also help users grasp the security associations with the real network environment based on the graph algorithm of the knowledge graph and intelligent reasoning. Furthermore, these actual network situations and knowledge bases can help managers improve policies more tailored to local conditions. This article also introduces the consistency detection process of typical cases of network security policies, demonstrating the practical details and effectiveness of this method. [ABSTRACT FROM AUTHOR]

At the core of practice for many non-Indigenous Australian social workers is learning how to work in deeply respectful, culturally appropriate ways alongside First Nations Peoples. Larrakia and Warumungu woman, and Northern Territory social work scholar, Dr Christine/Karen King’s (2011) writings provided the impetus for this inquiry. King (2011) invites social workers to critically reflect on their practice and worldviews. Responding to this invitation, 10 non-Indigenous social workers with policy, academic, and/or practice experience conducted a co-operative inquiry into the question “What have we learnt about practising social work in the Northern Territory, Australia?” We explored the uniqueness of the Northern Territory and identified three interwoven themes. The first theme grounds our practice to Place—on Country. Here, we identified the importance of engaging with the diverse histories of the Peoples we work alongside. Second, we reflected on our vulnerability as social workers and explored the limitations of our professional knowledge base for working with First Nations Peoples. The third theme relates to the importance of engaging with First Nations Peoples’ worldviews. We conclude by affirming the importance of social work practice being led by First Nations People on Country.IMPLICATIONS Social workers working in the Northern Territory alongside First Nations Peoples will benefit from engaging with vulnerability, reflexivity, and critical reflection on their values, understandings, language, and unexamined aspects of practice and cultural self.Social workers in the Northern Territory need to commit to ongoing learning and ground their practice with First Nations’ worldviews and wisdom.Social workers working in the Northern Territory alongside First Nations Peoples will benefit from engaging with vulnerability, reflexivity, and critical reflection on their values, understandings, language, and unexamined aspects of practice and cultural self.Social workers in the Northern Territory need to commit to ongoing learning and ground their practice with First Nations’ worldviews and wisdom. [ABSTRACT FROM AUTHOR]

Summary: In person‐centric applications, the prevalence of shared names significantly hampers document retrieval, web search, and database integration, highlighting the critical need for name disambiguation. Network embedding based unsupervised name disambiguation methods have received much attention due to their wide applicability and superior disambiguation performance. However, existing methods require complex feature engineering, such as extracting biographical and source content features or constructing supplementary features from external knowledge bases, which are unavailable in privacy‐preserving scenarios. Moreover, they may face challenges such as imbalanced node training or overlooking global statistical information during node embedding learning. In this article, we propose a method to tackle the name disambiguation problem based on high‐degree penalty and global statistical information using only relational data. First, we construct a weighted source similarity network based on multi‐hop collaborator relationships. Second, we employ a high‐degree penalty based global statistical network embedding model to learn low‐dimensional node embeddings and preserve the structural features of the network. Finally, we cluster the same sources using a joint clustering algorithm that does not require prior knowledge of the number of clusters. The experiment validates the effectiveness of the proposed method on two real‐life datasets. [ABSTRACT FROM AUTHOR]

Introduction Multimodal monitoring is the use of data from multiple physiological sensors combined in a way to provide individualized patient management. It is becoming commonplace in the civilian care of traumatic brain-injured patients. We hypothesized we could bring the technology to the battlefield using a noninvasive sensor suite and an artificial intelligence-based patient management guidance system. Methods Working with military medical personnel, we gathered requirements for a hand-held system that would adapt to the rapidly evolving field of neurocritical care. To select the optimal sensors, we developed a method to evaluate both the value of the sensor's measurement in managing brain injury and the burden to deploy that sensor in the battlefield. We called this the Value-Burden Analysis which resulted in a score weighted by the Role of Care. The Value was assessed using 7 criteria, 1 of which was the clinical value as assessed by a consensus of clinicians. The Burden was assessed using 16 factors such as size, weight, and ease of use. We evaluated and scored 17 sensors to test the assessment methodology. In addition, we developed a design for the guidance system, built a prototype, and tested the feasibility. Results The resulting architecture of the system was modular, requiring the development of an interoperable description of each component including sensors, guideline steps, medications, analytics, resources, and the context of care. A Knowledge Base was created to describe the interactions of the modules. A prototype test set-up demonstrated the feasibility of the system in that simulated physiological inputs would mimic the guidance provided by the current Clinical Practice Guidelines for Traumatic Brain Injury in Prolonged Care (CPG ID:63). The Value-Burden analysis yielded a ranking of sensors as well as sensor metadata useful in the Knowledge Base. Conclusion We developed a design and tested the feasibility of a system that would allow the use of physiological biomarkers as a management tool in forward care. A key feature is the modular design that allows the system to adapt to changes in sensors, resources, and context as well as to updates in guidelines as they are developed. Continued work consists of further validation of the concept with simulated scenarios. [ABSTRACT FROM AUTHOR]