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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]

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]

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]

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]

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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]

The application of hydrogen energy is affected by the safety of hydrogen storage system. To grasp the current status of research and application in the research field of hydrogen storage safety and explore its research development trend, data analysis techniques, such as co-occurrence, co-citation, and burst detection, were adopted to conduct bibliometric analysis of the relevant literature. The study results show that the research hotspots of hydrogen storage safety mainly concentrate on six aspects: the stability of metal hydride hydrogen storage materials, the study of thermal management and stress relief in metal hydride reactors, the safety analysis of hydrogen fuel cells and hydrogen leakage monitoring, the analysis of leakage and explosion risks in hydrogen refueling stations, the study of filling pressure and temperature distribution in hydrogen storage tanks, and the study of safety issues in underground hydrogen storage. Mg-based hydride stability and underground hydrogen storage risk analyses are current research frontiers in hydrogen storage safety. • This paper presents the development of hydrogen storage in safety research. • 1552 papers are visualized and analyzed using bibliometrics. • Identifying research frontiers by combining timeline view with burst detection. • Knowledge bases, research hotspots, and frontiers are discussed. • The current challenges and perspectives for the future are proposed. [ABSTRACT FROM AUTHOR]

Background: Distantly supervised relation extraction (DSRE) aims to identify semantic relations in large-scale texts automatically labeled via knowledge base alignment. It has garnered significant attention due to its high efficiency, but existing methods are plagued by noise at both the word and sentence level and fail to address these issues adequately. The former level of noise arises from the large proportion of irrelevant words within sentences, while noise at the latter level is caused by inaccurate relation labels for various sentences. Method: We propose a novel multi-level noise reduction neural network (MLNRNN) to tackle both issues by mitigating the impact of multi-level noise. We first build an iterative keyword semantic aggregator (IKSA) to remove noisy words, and capture distinctive features of sentences by aggregating the information of keywords. Next, we implement multi-objective multi-instance learning (MOMIL) to reduce the impact of incorrect labels in sentences by identifying the cluster of correctly labeled instances. Meanwhile, we leverage mislabeled sentences with cross-level contrastive learning (CCL) to further enhance the classification capability of the extractor. Results: Comprehensive experimental results on two DSRE benchmark datasets demonstrated that the MLNRNN outperformed state-of-the-art methods for distantly supervised relation extraction in almost all cases. Conclusions: The proposed MLNRNN effectively addresses both word- and sentence-level noise, providing a significant improvement in relation extraction performance under distant supervision. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN's goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future. [ABSTRACT FROM AUTHOR]

• A comprehensive list of risk parameters impacting the safety assessment of tower crane operations is put forward. • A detailed list of safety risks threatening the workers working with tower crane is put forward. • A hybrid safety risk assessment model for analyzing and evaluating related safety risks is developed. • A Knowledge-based decision support system safety risk assessment for automating the whole related assessment procedure is developed. • The efficacy of the developed prototype is validated through its application to several construction projects. Introduction: Tower cranes are commonly employed in construction projects, despite presenting significant hazards to the workforce involved. Method: To address these safety concerns, a Knowledge-Based Decision-Support System for Safety Risk Assessment (KBDSS-SRA) has been developed. The system's capacity to thoroughly evaluate associated risks is illustrated through its utilization in various construction endeavors. Results: The system accomplishes the following goals: (1) compiles essential risk factors specific to tower crane operations, (2) identifies critical safety risks that jeopardize worker well-being, (3) examines and assesses the identified safety risks, and (4) automates the labor-intensive and error-prone processes of safety risk assessment. The KBDSS-SRA assists safety management personnel in formulating well-grounded decisions and implementing effective measures to enhance the safety of tower crane operations. Practical Applications: This is facilitated by an advanced computerized tool that underscores the paramount significance of safety risks and suggests strategies for their future mitigation. [ABSTRACT FROM AUTHOR]

This article examines the concept of servant leadership and its impact on organizational trust and conflict resolution. It draws inspiration from Herman Hesse's novel "The Journey to the East" and Robert Greenleaf's work on servant leadership. The article emphasizes the importance of trust within organizations and the role of leaders in establishing trust. It also discusses the need for effective conflict resolution strategies, particularly in relation to affective conflict. The author proposes a theoretical model and evidence-based propositions to illustrate the connections between servant leadership, organizational trust, and affective conflict. The article introduces the Walk-In-The-Woods process as a structured approach to conflict resolution that promotes principled negotiation and a win-win focus. It consists of four steps: revealing self-interests, enlarging interests, exploring enlightened interests, and aligning interests. The process encourages transparency, understanding, and problem-solving to reach mutually beneficial solutions. A case study involving a merger dispute between two academic medical centers demonstrates the effectiveness of the Walk-In-The-Woods method. The article highlights the benefits of the process for servant leaders, but acknowledges the need for further research to support its potential advantages. [Extracted from the article]

Understanding what diversity of small‐scale fisheries translates to in practice, and what this means for management regimes seeking sustainability, continues to be a challenging undertaking. This is particularly so in the tropical Pacific Islands region, where small‐scale coastal fisheries play a significant role in domestic food and livelihood systems. A renewed regional policy focus on supporting coastal fisheries, combined with momentum built from a decades‐long 'Pacific renaissance' in community‐based fisheries management approaches, has increased resourcing and support for coastal fishery data collection and knowledge production. In this context, there is growing demand to explicitly characterise diversity and complexity of community‐based coastal fisheries to inform how national co‐management programs can adequately support the many communities within national constituencies. This study presents findings from a community‐based coastal fisheries monitoring programme implemented in ten communities across Kiribati and Vanuatu between 2019 and 2021. Findings illustrate the intra‐ and inter‐country diversity of contextual drivers, fishing practices, and fisher participation. We discuss the implications of this enhanced understanding of community‐based fisheries for applied co‐management practice in these two countries. In doing so, we add to a growing knowledge base about fishing practices in Pacific Island coastal communities and elucidate avenues through which to incorporate this knowledge into adaptive co‐management practice. [ABSTRACT FROM AUTHOR]

Recently, there have been calls to build a more inclusive knowledge base of the home numeracy environment (HNE) by diversifying the populations in our descriptive research. Given that Latine children are the fastest-growing population in the U.S. it is of the utmost importance to include Latine families and children in these efforts. The present study examined how U.S. Latine families support their children’s early math learning by: (1) identifying distinct numeracy engagement patterns, and (2) examining if these patterns were related to families’ demographic characteristics. Participants were 488 caregivers (Mchild-age = 4.29 years, SD = 1.23). We found two profiles of Latine family numeracy engagement: a Balanced Numeracy Engagement and a Non-Play-Numeracy Engagement. Caregivers’ formal educational level and relationship to the child were significantly associated with the profiles. Findings reflect the importance of understanding the variability within Latine families when engaging in numeracy activities. [ABSTRACT FROM AUTHOR]

In today's knowledge-driven economy, collaboration among stakeholders is essential for the framing of innovative trends, with knowledge-intensive business services (KIBS) playing a core role in addressing market demand. Users' involvement in shaping products and services has been considered in innovation ecosystem frameworks. Fewer risks in service/product development, and more sustainability and market acceptance, are a few of the benefits arising from including the user community (UC) in innovation partnerships. However, the need for resources, absorptive capacity and tacit knowledge, among other capabilities, is often a reason for overlooking this important contributor. KIBS possess a vast knowledge base, cater to digital tools, and mediate and propel innovation with different partners, benefiting from exclusive cognitive proximity to remix extant knowledge with emergent information from communities into new products and services. The aim of this study is to assess and quantify the effect of the collaboration with UC through three active forms of collaboration (co-creation, mass customization, and personalization) on different innovation types developed in KIBS. The significance of the user community was proven across all innovation types. Robustness analysis confirmed the results for both P-KIBS and T-KIBS. P-KIBS may be better suited to co-creation policies for product and service innovation, personalization of processes, and organizational and marketing innovations. T-KIBS can focus on mass customization, ensuring good innovation success. Additionally, co-creation with user community is best for product innovation. [ABSTRACT FROM AUTHOR]

An essential component of case-based reasoning (CbR) is the similarity function. The existence of incorrect similarity values, one of the primary issues of similarity function, occurs when two cases seem to have a lot in similarity but actually differ greatly from one another. The aim of this study is to suggest a fuzzy similarity function that might be used with CbR, which has been executed in three primary phases. Firstly, a knowledge base is created. Secondly, the inference engines are created. To determine fuzzy similarity values, the proposed approach employs Graded Mean Integration Representation (GMIR), which takes into consideration the impact of each evidence on the case. Finally, model testing is determined by comparing GMIR with the additive function and additive weighting and using them as the sensitivity test. A comparison of similarity methods was carried out using the k-NN, Euclidean, and Pearson similarity. Expert evaluation carries out external validity checking. The degree of similarity between the CbR output solution and the expert solution is determined using the Intuitive Fuzzy Weighted Arithmetic Mean (IFWAM). The results demonstrate that the GMIR function applied with thresholds of 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8 provides the fewest instances of similar cases when compared to the additive and additive weighting approaches. GMIR is able to provide a variety of alternatives using the threshold value. IFWAM performs 93.33% when used in 15 test scenarios, and only one of the solutions from the CbR output differs from that offered by the pshycologs. The main reason is the challenge of selecting keywords for every item of evidence provided. When CbR's validity was examined, it was also demonstrated that IFWAM was reliable in estimating the degree of similarity between CbR output and expert solutions. [ABSTRACT FROM AUTHOR]

The paper describes a method for developing decision support systems for prescribing complex treatment. It is based on the creation of a task-oriented shell, the key component of which is a generalized ontology that allows one to generate knowledge about various types of treatment (medicinal, restorative, surgical) regardless of the branch of medicine. This makes it possible to create a single solver, which provides a significant reduction in system development costs. The work describes the adaptation of the ontology to various types of treatment and the terminology of the knowledge base developer—a domain expert. The ontology is implemented on the IACPaaS cloud platform and is currently actively used to create knowledge bases in various fields of medicine. [ABSTRACT FROM AUTHOR]

This article discusses an approach to enhancing the efficiency of database queries to optimize data retrieval and processing times from high-load database management systems. The proposed method involves forming a knowledge base for an expert system in sets of rules that optimize data retrieval time from relational databases. The set of rules, which increase data processing efficiency, addresses several issues: lack of profiling, performance problems caused by merging the structure of queries with the algorithmic part of the task, and the execution structure of queries. Additionally, the paper presents results from experiments on data processing for the information system of the Ilyushin Aviation Complex–Aviastar Public Joint Stock Company. [ABSTRACT FROM AUTHOR]

This article presents mechanisms to support the formation and execution of ontological queries for an orgraphic coherent two-level model of information units (knowledge and data bases). The application of the proposed approaches in the processing of knowledge bases should make it possible to make such queries without intermediaries (programmers). The implementation of the proposed solutions on a platform designed for the creation and use of cloud-based artificial intelligence systems is also described. [ABSTRACT FROM AUTHOR]

Ontological patterns are a tool for reducing the labor intensity of developing intelligent software systems. The paper presents a pattern for a class of diagnostic tasks. It contains a complex of domain-independent semantic structures for the formation of knowledge, input documents, diagnostic results, and its explanation. The complex of structures is supplemented by means of their adaptation to specialized domain terms. The universal diagnostic solver works with knowledge and other information generated in accordance with these semantic structures. The result of its reasoning is a set of unrejected versions of the diagnosis (malfunction, anomalous process, deviation). Thanks to the new adaptation method, knowledge and data are formed and viewed in the terminology (and markup) of the domain. At the same time, problem-oriented (diagnostic) logical inference does not depend on these particular terms. Therefore, diagnostic software systems for various practical fields of activity are effectively produced on the basis of a single pattern and solver. [ABSTRACT FROM AUTHOR]

Objectives The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking. Materials and Methods For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE). Results Our pioneering NLP system designed for this challenge secured first place in Phase I—NER and second place in Phase II—relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models. Discussion and Conclusion Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research. [ABSTRACT FROM AUTHOR]

Many parameters and complex boundaries are involved in the spatial arrangement of an underground powerhouse in a hydropower station, necessitating referencing many relevant cases and specifications. However, in practical applications, retrieving such cases or specifications is difficult, and there is a lack of knowledge regarding cascading logic among design parameters. To address this issue, this study proposes a novel methodology for constructing a targeted knowledge graph, in this case, a knowledge graph for building information modeling (BIM) for underground powerhouses in hydropower plants. Subsequently, based on this knowledge graph, this study develops a question-and-answer (Q&A) system to facilitate subsequent applications. First, the ontology skeleton of the spatial arrangement design of an underground powerhouse in a hydropower station, that represents the knowledge organization structure of the knowledge graph, is constructed by carefully analyzing the requirements for intelligent modeling of underground powerhouses. A large volume of unstructured data is identified based on the optical character recognition (OCR) technology; the collected data are divided into words to extract correlation knowledge using THU Lexical Analyzer for Chinese (THULAC). Subsequently, the knowledge triad of the spatial arrangement of the underground powerhouse is extracted based on ChatGPT and stored in Neo4j, a knowledge base, to build a knowledge graph. Finally, the knowledge graph is employed to realize the query of knowledge and parameter recommendation to assist the digital intelligent design of the spatial arrangement of an underground powerhouse in a pumped storage hydropower station. [ABSTRACT FROM AUTHOR]

E-learning has become an emerging approach to delivering knowledge and information to students. It presents significant challenges to sharing and understanding learners' knowledge, inquiries, interest, and needs. Educational organizations are expected to apply the appropriate practice to address knowledge-sharing challenges in the e-learning environment. In this study, through a systematic literature review, we intend to identify and synthesize knowledge-sharing challenges and practices as well as classify the most discussed challenges and practices in various contextual settings. The findings classify the knowledge-sharing challenges and practices in e-learning from four perspectives: organization, individual, knowledge and technology. This study aims to build a knowledge base to support future research and effective knowledge-sharing practices in the e-learning environment. [ABSTRACT FROM AUTHOR]

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Cyberattacks disrupt systems, leaving critical infrastructure vulnerable to adversaries, especially during natural disasters. Furthermore, when both a cyberattack and a natural disaster occur concurrently, there are limited tools to ensure further damage beyond the physical is not experienced in crucial societal systems, such as emergency services, which need to operate during any type of hazard. Two prominent knowledge bases for adversary attacks in the cybersecurity community are the MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) Enterprise Matrix and the National Institute of Standards and Technology (NIST) Cybersecurity Framework. Existing processes to derive possible attack methodologies in general from such sources are largely manual and time-consuming. It is essential to automate the information retrieval process to improve efficiency and free up resources for identifying potential cyberattacks. It is also important to identify preventive measures with both human-made and natural hazards in mind. We propose an approach that incorporates Natural Language Processing (NLP) to automatically generate sets of attack paths from the technique descriptions in the Matrix, with both cyber-based and emergency management–based contexts, then map these techniques to the Framework to identify potential relationships between techniques and outlined protective actions. The approach generates outputs showing potential pathways an adversary can take to infiltrate a system, and its respective defense action based on similarity measures. The similarities between techniques and the Framework are evaluated with p-values to determine relevancy of pairings. The results of this study provide an approach to more quickly and effectively assess potential cyberattacks toward protecting critical infrastructure that can be utilized in broader vulnerability analyses, considering contextual data to represent both cyber and natural disaster events. [ABSTRACT FROM AUTHOR]

For the first time, mathematical models for evaluating biomedical images were obtained using fuzzy set methods on the basis of expert knowledge bases, thus making it possible to carry out complex qualitative diagnostics and increase the reliability and efficiency of diagnosis. A methodology for analyzing biomedical images was formed based on the fuzzy set apparatus, which allowed a fuller assessment of the level of disease for glaucoma patients. Aspects of the practical implementation of the optical–electronic system for biomedical information processing were considered. An algorithm and an optical–electronic system for biomedical image analysis are proposed, and they can be used to increase the informativeness and reliability in the diagnosis of eye pathologies and glaucoma in particular. [ABSTRACT FROM AUTHOR]

Critical to navigating research literature is ensuring the inclusion of relevant sources while filtering out irrelevant ones. Selecting suitable references demands careful attention and methodological rigor. This review article presents a systematic approach consisting of 11 sequential steps for constructing a reference list, ranging from broad initial searches to excluding irrelevant references. It emphasizes refining methodologies to develop a coherent list of references aligned with the study's scope, bolstering the knowledge base, and facilitating subsequent analyses. Urban planners and designers can apply these steps in database searches to create robust reference lists, thereby enhancing the quality and reliability of their research and ensuring future adaptability. [ABSTRACT FROM AUTHOR]

Subevent relation identification (SRI) is a challenging natural language processing task of great value for knowledge acquisition and reasoning. Given an event pair, previous work mainly defines SRI as a classification task and usually relies on hand-crafted features extracted from a limited context. However, there are lots of evidence information in external knowledge bases which are not well explored by prior work but helpful for this task. To fill this gap, we propose to reason the subevent relation based on plentiful evidence information in addition to limited context features. Specifically, we notice that evidence information together with the input event pair can be organized into a heterogeneous event graph, which comprises various types of event relations. To facilitate this, we present a heterogeneous graph attention model with mix-hop reasoning mechanism to reason subevent relation over heterogeneous event graph. In particular, our model allows nodes to capture more extensive and intact event knowledge by mixing feature representations of neighbors at multiple distances to aggregate information from diverse types of neighbors. Moreover, to explicitly model the hierarchical relations between event pairs and to improve the model consistency, we devise a novel hierarchy loss function for the proposed model. Our model is evaluated on two annotated datasets with subevent hierarchies: SeRI and HiEve. Experimental results show that our approach can outperform state-of-the-art baseline methods. Further evaluation suggests that reasoning paths in the heterogeneous graph can regard as a reasonable explanation for the prediction result. [ABSTRACT FROM AUTHOR]

A new legally binding agreement for the conservation and sustainable use of marine biodiversity beyond national jurisdiction (BBNJ) was adopted by consensus on 19th June, 2023. Setting new regulations and filling regulatory gaps of the United Nations Convention on the Law of the Sea are expected to be informed by "best available science". It is critical to understand how science entered the negotiations, which defined the global scientific knowledge base of decision-makers. This paper presents various pathways over which scientific input entered the BBNJ negotiations, using empirical data, collected through collaborative event ethnography, including participant observation and semi-structured interviews at the BBNJ negotiation site. Results show that scientific input in the BBNJ negotiations was not systematic and transparent but dependent on (a) available national scientific capacity, (b) financial resources, (c) established contacts and (d) active involvement of actors. Results of the study call for formalised science-policy interfaces, initiated by the UN Secretariat to guarantee a global knowledge base for decision-making. Keywords: international negotiations; United Nations; marine biodiversity; BBNJ; ocean protection; science-policy interfaces. [ABSTRACT FROM AUTHOR]

The illegal wildlife trade (IWT) is a global phenomenon that adversely affects biodiversity and human well-being. Understanding how institutions manage this trade is crucial for reducing its negative impacts. Latin America has the fewest IWT studies globally; thus, science in support of more effective institutional management of IWT is limited. This study aims to bridge the researcher-practitioner gap by providing applicable results and involving control institutions. To this end, we examine the strengths and weaknesses of Uruguay's institutions in addressing this issue, introducing the concept of institutional management competence to the knowledge base about IWT. Based on this case study, we aimed to generate inputs to guide policymakers in achieving better control of the IWT, contributing to reduce the researcher-practitioner gap. From an interdisciplinary perspective that articulates qualitative and quantitative methods, the study presents the following results: (a) Uruguay's network for addressing illegal wildlife trade involves numerous institutions whose articulation has a high degree of informality; (b) these institutions address different stages of trafficking based on their roles, jurisdiction, and engagement; (c) main weaknesses include insufficient state-level prioritisation, weak institutional coordination, inadequate training, insufficient infrastructure, space and personnel to handle the volume of seized animals, lack of proper facilities for seized animals, and a need for better-organised information. Our results help shed light on the IWT management structures in Uruguay and identifies where direct improvements can be made to strengthen the institutional responses to global IWT. [ABSTRACT FROM AUTHOR]