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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]

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]

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]

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]

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]

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]

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]

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]

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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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]

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]

Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability. Database URL https://zenodo.org/records/7886998. [ABSTRACT FROM AUTHOR]

Postoperative delayed gastric emptying is a prevalent complication following surgical procedures, imposing heavy physical and financial burdens on patients. However, current treatment options remain suboptimal. In recent years, an increasing number of studies have highlighted that the gut microbiota and its metabolites are closely associated with postoperative complications. Various factors can disrupt the gut microbiome after surgery. This review discusses the potential mechanisms by which the gut microbiota and their metabolites may contribute to the pathogenesis of postoperative delayed gastric emptying. However, the current knowledge base is limited in terms of fully understanding the exact mechanisms involved. It is therefore evident that further research is required to fully elucidate the role of the gut microbiome in postoperative delayed gastric emptying, with the aim of uncovering new possibilities for preventive measures and therapeutic treatments. [ABSTRACT FROM AUTHOR]

Simulated data play an important role in SAR target recognition, particularly under zero-shot learning (ZSL) conditions caused by the lack of training samples. The traditional SAR simulation method is based on manually constructing target 3D models for electromagnetic simulation, which is costly and limited by the target's prior knowledge base. Also, the unavoidable discrepancy between simulated SAR and measured SAR makes the traditional simulation method more limited for target recognition. This paper proposes an innovative SAR simulation method based on a visual language model and generative diffusion model by extracting target semantic information from optical remote sensing images and transforming it into a 3D model for SAR simulation to address the challenge of SAR target recognition under ZSL conditions. Additionally, to reduce the domain shift between the simulated domain and the measured domain, we propose a domain adaptation method based on dynamic weight domain loss and classification loss. The effectiveness of semantic information-based 3D models has been validated on the MSTAR dataset and the feasibility of the proposed framework has been validated on the self-built civilian vehicle dataset. The experimental results demonstrate that the first proposed SAR simulation method based on a visual language model and generative diffusion model can effectively improve target recognition performance under ZSL conditions. [ABSTRACT FROM AUTHOR]

Motivation Biomedical entity linking (BEL) is the task of grounding entity mentions to a given knowledge base (KB). Recently, neural name-based methods, system identifying the most appropriate name in the KB for a given mention using neural network (either via dense retrieval or autoregressive modeling), achieved remarkable results for the task, without requiring manual tuning or definition of domain/entity-specific rules. However, as name-based methods directly return KB names, they cannot cope with homonyms, i.e. different KB entities sharing the exact same name. This significantly affects their performance for KBs where homonyms account for a large amount of entity mentions (e.g. UMLS and NCBI Gene). Results We present BELHD (Biomedical Entity Linking with Homonym Disambiguation), a new name-based method that copes with this challenge. BELHD builds upon the BioSyn model with two crucial extensions. First, it performs pre-processing of the KB, during which it expands homonyms with a specifically constructed disambiguating string, thus enforcing unique linking decisions. Second, it introduces candidate sharing, a novel strategy that strengthens the overall training signal by including similar mentions from the same document as positive or negative examples, according to their corresponding KB identifier. Experiments with 10 corpora and 5 entity types show that BELHD improves upon current neural state-of-the-art approaches, achieving the best results in 6 out of 10 corpora with an average improvement of 4.55pp recall@1. Furthermore, the KB preprocessing is orthogonal to the prediction model and thus can also improve other neural methods, which we exemplify for GenBioEL, a generative name-based BEL approach. Availability and implementation The code to reproduce our experiments can be found at: https://github.com/sg-wbi/belhd. [ABSTRACT FROM AUTHOR]

Data supplied by mobile phones have become the basis for identifying meaningful places frequently visited by individuals. In this study, we introduce SAMPLID, a new Supervised Approach for Meaningful Place Identification, based on providing a knowledge base focused on the specific problem we aim to solve (e.g., home/work identification). This approach allows to tackle place identification from a supervised perspective, offering an alternative to unsupervised clustering techniques. These clustering techniques rely on data characteristics that may not always be directly related to classification objectives. Our results, using mobility data provided by call detail records (CDRs) from Milan, demonstrate superior performance compared to applying clustering techniques. For all types of CDRs, the best results are obtained with the 20 × 20 subgrid, indicating that the model performs better when supplied with information from neighboring cells with a close spatial relationship, establishing neighborhood relationships that allow the model to clearly learn to identify transitions between cells of different types. Considering that it is common for a place or cell to be labeled in multiple categories at once, this supervised approach opens the door to addressing the identification of meaningful places from a multi-label perspective, which is difficult to achieve using classical unsupervised methods. [ABSTRACT FROM AUTHOR]

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38 % on the full dataset and 47 % on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4 % after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data. [ABSTRACT FROM AUTHOR]

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity (MDI) method. To establish productivity prediction models, linear regression (LR), random forest regression (RF), support vector regression (SVR), backpropagation (BP), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms are used. Their performance is evaluated using the coefficient of determination (R²), explained variance score (EV), mean squared error (MSE), and mean absolute error (MAE) metrics. The LightGBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query, oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development. [ABSTRACT FROM AUTHOR]

This study endeavors to formulate a comprehensive methodology for establishing a Geological Knowledge Base (GKB) tailored to fracture-cavity reservoir outcrops within the North Tarim Basin. The acquisition of quantitative geological parameters was accomplished through diverse means such as outcrop observations, thin section studies, unmanned aerial vehicle scanning, and high-resolution cameras. Subsequently, a three-dimensional digital outcrop model was generated, and the parameters were standardized. An assessment of traditional geological knowledge was conducted to delineate the knowledge framework, content, and system of the GKB. The basic parameter knowledge was extracted using multiscale fine characterization techniques, including core statistics, field observations, and microscopic thin section analysis. Key mechanism knowledge was identified by integrating trace elements from filling, isotope geochemical tests, and water-rock simulation experiments. Significant representational knowledge was then extracted by employing various methods such as multiple linear regression, neural network technology, and discriminant classification. Subsequently, an analogy study was performed on the karst fracture-cavity system (KFCS) in both outcrop and underground reservoir settings. The results underscored several key findings: (1) Utilization of a diverse range of techniques, including outcrop observations, core statistics, unmanned aerial vehicle scanning, high-resolution cameras, thin section analysis, and electron scanning imaging, enabled the acquisition and standardization of data. This facilitated effective management and integration of geological parameter data from multiple sources and scales. (2) The GKB for fracture-cavity reservoir outcrops, encompassing basic parameter knowledge, key mechanism knowledge, and significant representational knowledge, provides robust data support and systematic geological insights for the intricate and in-depth examination of the genetic mechanisms of fracture-cavity reservoirs. (3) The developmental characteristics of fracturecavities in karst outcrops offer effective, efficient, and accurate guidance for fracture-cavity research in underground karst reservoirs. The outlined construction method of the outcrop geological knowledge base is applicable to various fracture-cavity reservoirs in different layers and regions worldwide. [ABSTRACT FROM AUTHOR]

For confidential and sensitive electronic documents within enterprises and organizations, failure to conduct proper checks before sending can easily lead to incidents such as security degradation. Sensitive information transmission has become one of the main ways of internal data leakage. However, existing methods or systems cannot extract hidden data and do not support mining the binary structure of hidden information in files. In this paper, an expert system for mining electronic document hidden information is designed for various office documents, compressed files, and image files. This system can quickly mine various forms of concealment in more than 40 common types of electronic documents and extract hidden information, such as file type tampering, encryption concealment, structure concealment, redundant data concealment, etc. Additionally, feature information in the binary structure of the document is extracted to form a feature information base. Subsequently, an expert knowledge base is constructed. Finally, a hidden information mining engine is designed using the knowledge base to realize the security control of corresponding outgoing files with good expansibility and integration. By controlling the exit of documents through scanning for sensitive information contained within them, the security level contents can be obtained effectively, avoiding data leakage by technical means while also facilitating forensics. The actual test result proves that this system can quickly mine various means used to conceal information, extract their respective information, and provide a fast, practical diagnostic way for outgoing control over these electronic documents. [ABSTRACT FROM AUTHOR]

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene–gene interaction. The main purpose of this study is to develop extraction methods for gene–gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene–gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene–gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene–gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene–gene interactions. In this way, the results of this study are expected to help achieve precision medicine. [ABSTRACT FROM AUTHOR]

Despite being very old, the mining industry continues to be one of the major sources of pollution, with more people killed or injured than in all other industries. Prevention of incidents/accidents on machinery in mining pits and the issues of operator safety on mining machinery largely depend on the ergonomic adaptation of the workplace, compliance with safety procedures and policies, and organizational and other influential factors. Evidently, scarce consideration of those factors in the available literature has not given satisfactory results till now. The aim of this paper is to first set up a comprehensive model based on ergonomic factors and contextual theory, which takes into account all the influencing factors on the occurrence of incidents/accidents and represents a complex system of interdependence of influential variables of diverse, mostly stochastic nature, and then design a software solution on the given basis. In this research, based on the extensive data collected, a model was generated using the structural equations modelling methodology, which was then used to design the reasoning logic in the expert system for mitigating the risks of the operation of mining machines. An innovative solution incorporating a mathematical model of the interdependence of influential variables into the stored knowledge base offers a decision support system that provides recommendations for the maintenance of a particular mining machine, depending on the assessment of model factors in a specific decision-making situation at the higher organizational level and ergonomic suitability for the operator at the lower organizational level, and, in that manner, enables the mitigating of risky/unwanted events. [ABSTRACT FROM AUTHOR]

Monolayer and few-layer CoPS3 have been successfully synthesized via an Au-assisted exfoliation technique, with their transport properties subsequently investigated by conductive atomic force microscopy. Rectification effects can be observed in CoPS3 nanosheets due to the Schottky junction formation at the Au/CoPS3 interface, especially in configurations comprising two or more layer flakes. The distinct transport properties between monolayer and few-layer samples demonstrate that the charge transport behavior in the vertical direction is associated with the van der Waals gap. The calculated electron affinity for CoPS3 is about 4.84 eV, as revealed by the thermionic emission model. The results obtained contribute to the knowledge base concerning the transport characteristics of ultrathin MPX3, facilitating further exploration of the transport behavior of these materials and their potential applications in novel electronic devices. [ABSTRACT FROM AUTHOR]

The purpose of this work is to develop an IT diagnostic system for Parkinson's disease (PD) with remote access based on the Internet of Things (IoT) network.Methods. The authors have developed a method for complex recognition of Parkinson's disease using machine learning, based on markers of voice analysis and changes in patient movements on known datasets. In the architecture of the Internet of Things network, a smartphone is the point of initial data collection and preprocessing, including extracting features from an audio recording of the patient's voice and his motor activity. Data is transmitted via a local Flask server, which acts as a channel for sending functional data to the Open Semantic Technology for Intelligent Systems (OSTIS) server. The OSTIS server processes the data received from the local Flask server and uses a neural network prediction agent to recognize BP. This agent downloads features and makes predictions based on a trained neural network, linking these predictions with knowledge in the OSTIS system, and stores them in a database.The result of the study is the architecture and algorithms of the IoT network. The workflow of the entire system includes data collection and preprocessing by the Internet of Things device, subsequent data transfer to the local Flask server, further forwarding to the OSTIS server, processing of the neural network model by a neural network predictor agent and, ultimately, linking the processed results to the knowledge graph and storing them in the system.The BP remote IT diagnostics system provides real-time processing of patient data, recognition of disease signs on the Internet of Things, support for advanced analysis and decision-making for further treatment.

Abstract The proposed smart system for Student Performance Assessment (SPA) is a system that evaluates students' knowledge and skill attainment in a specific course by measuring their achievements of the Course Learning Outcomes (CLOs). The instructor defines the aspects, weights, and rating scale used by SPA to analyze each course. The system calculates the average of students' marks in each learning outcome and compares them with the CLO targets and scores to determine the effectiveness of the teaching and learning methods used. The system uses facts and rules extracted from the course syllabus and Bloom's Taxonomy to build its knowledge base. This paper presents the development of the SPA inference engine, which is used to find CLO targets based on the course level. The inference engine uses efficient procedures and a prediction process to determine the correct target and score, providing a reliable and understandable methodology for reasoning about the information in the knowledge base and formulating conclusions. SPA is a highly responsive and intelligent system that can be a valuable tool for measuring students' achievements. Its characteristics include high performance, reliability, and intelligibility, and its combination of cognitive systems and cognitive theory has led to remarkable progress in measuring student performance. Limitations include dependency on accurate course content and initial setup time, potential bias in CLO weight assignments, challenges in integrating SPA with existing institutional databases, the need for continuous updates to the knowledge base to reflect curriculum changes, and potential resistance from educators to adopt new technologies. Future improvements could involve adaptive learning integrations, enhanced user interfaces, and broader applicability across diverse educational settings.

Highway operation and maintenance (O&M) systems exhibit characteristics of multisubsystem correlations and interactions among multiple disaster-causing factors over time. Traditional risk-centered approaches, which focus on postevent effects and causes, fail to address these challenges adequately. This study aims to devise a methodological framework that is multisubsystem and multifactor dynamic in nature, intended for assessing, analyzing, and enhancing the resilience of highway O&M systems. This framework comprises a qualitative component, utilizing fault tree analysis (FTA) to identify resilience factors within each subsystem, and a quantitative aspect, employing the improved human factors analysis and classification system (HFACS) and fuzzy dynamic Bayesian networks (FDBNs) for resilience assessment. Subsequently, a case study of the Shenyang-Dalian Highway (SDH) in China was employed to demonstrate the application of this methodological framework. The findings highlight the need for enhancing the resilience of highway O&M systems, particularly by focusing on personnel and equipment factors within the absorption and resistance subsystems and prioritizing organizational and personnel factors within the recovery and adaptation subsystems. Furthermore, dynamic strategies tailored to different O&M objectives were proposed. These frameworks contribute to the existing knowledge base of highway O&M systems by integrating resilience theory into performance evaluation and improvement efforts amid uncertainty. [ABSTRACT FROM AUTHOR]

Abstract Background Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool. Methods We structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology’s creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool’s functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool’s productive utilization. Results The core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool’s evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise. Conclusions Through the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it’s crucial to address potential modeling issues that are associated with the Ontology of Adverse Events.

Background: Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool. Methods: We structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology's creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool's functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool's productive utilization. Results: The core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool's evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise. Conclusions: Through the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it's crucial to address potential modeling issues that are associated with the Ontology of Adverse Events. [ABSTRACT FROM AUTHOR]

Building on resource-based view (RBV) and contingency theory (CT), the purpose of this study is to explore how entrepreneurial alertness (EA) can promote entrepreneurial bricolage (EB), and then contributes to improving the new venture performance, with the environmental dynamism as the moderating variable. This study adopted a hierarchical regression analysis to test our hypotheses, using data from 463 founders of new ventures in China. The study found that: (1) EA has a positive effect on new venture performance, (2) EB mediates the effect of EA on new venture performance, (3) environmental dynamism (ED) moderates the relationship between EB and new venture performance, and (4) the mediating effect of EB is also moderated by ED. This article provides valuable and novel insights both theoretically and practically, that makes outstanding contributions to improving the performance of new ventures. Plain language summary: Purpose: This study aims to explore how entrepreneurial alertness can promote entrepreneurial bricolage, and then contribute to improving the new venture performance, with the environmental dynamism as the moderating variable. Methods: We adopted a hierarchical regression analysis to test our hypotheses, using data from 463 founders of new ventures in China. Conclusions: Entrepreneurial alertness has a positive effect on new venture performance, entrepreneurial bricolage mediates the effect of entrepreneurial alertness on new venture performance, and environmental dynamism moderates the relationship between entrepreneurial bricolage and new venture performance. Moreover, the mediating effect of entrepreneurial bricolage is also moderated by environmental dynamism. Implications: This study (1) completes the theoretical model of how entrepreneurial alertness influences performance by studying the influence mechanism of entrepreneurial alertness on the performance in the context of new ventures, (2) provides novel insights to the entrepreneurship literature by investigating the mediating effect of entrepreneurial bricolage, and (3) contributes to strengthening the understanding of entrepreneurship process and expanding the knowledge base regarding contingency theory in the entrepreneurship field. Limitations: The cross-sectional data herein suggest that the conclusions may not reflect causality. Therefore, the dynamic model should be tested using longitudinal data in the future. This study focuses on the mechanism of entrepreneurial alertness on the performance in the context of new ventures, without considering the different stages of firm development, which should be tested in the future. CMV could still present a limitation, and future research should better control CMV via using questionnaires from multiple data sources. [ABSTRACT FROM AUTHOR]

Effective safety management is crucial in the construction industry. The growing interest in employing Knowledge Graphs (KGs) for safety management in construction is driven by the need for efficient computing-aided safety practices. This paper systematically reviews the literature related to automating safety management processes through knowledge base systems, focusing on the creation and utilization of KGs for construction safety. It captures current methodologies for developing and using KGs in construction safety management, outlining the techniques for each phase of KG development, including scope identification, integration of external data, ontological modeling, data extraction, and KG completion. This provides structured guidance on building a KG for safety management. Moreover, this paper discusses the challenges and limitations that hinder the wider adoption of KGs in construction safety management, leading to the identification of goals and considerations for future research. [ABSTRACT FROM AUTHOR]

This paper disseminates our research findings that we conducted on university students in the years 2021, 2022, and 2023, with the year 2021 taken as the base year. Our research mined and excavated a concealed prevalence of social anxiety as an important and crucial facet of study anxiety in the university students of Pakistan. Using the Liebowitz Social Anxiety Scale (LSAS), we found a significant increase in the social anxiety level among university students in the past three years after the COVID-19 lockdown. Our data showed that the 'very severe anxiety' level soared up to 52.94% in the year 2023 from just 5.98% in the year 2021, showing a net increase of 47.06%. Statistical analyses demonstrate noteworthy differences in the overall social anxiety levels among the students, reaching significance at the 5% level and a discernable upward trend in the social anxiety levels as study anxiety. We also employed predictive analytics, including binary classifiers and generalized linear models with a 95% confidence interval, to identify individuals at risk. This study highlights a dynamic shift with escalating social anxiety levels among the university students and thus emphasizing its awareness, which is significantly important for the timely intervention, potentially preventing symptom escalation and improving outcomes. [ABSTRACT FROM AUTHOR]

Background. Medical information retrieval is becoming more crucial as a way to support decision-making and improve access to knowledge and information for doctors and other specialists in the field. By the incorporation of medical domain knowledge for relevance evaluation, integrating the medical knowledge bases has the potential to enhance information retrieval performance. The system uses various search algorithms and techniques to provide accurate and comprehensive results to medical professionals and researchers. Medical information retrieval systems are particularly useful for healthcare providers who need to stay up-to-date with the latest medical research, guidelines, and treatment options. The purpose was to study the use of medical information systems to improve the quality of medical care for patients with diabetes. Materials and methods. To assess the relevance of research using information systems in the SCOPUS database, an analytical query was formulated: ALL (“Diabetes”) AND (ALL (“Constant monitoring of glucose”) OR ALL (“Coefficient of variability of glucose”) OR ALL (“Time in the given target range”) OR ALL (“Time above range”) OR ALL (“Time below range”) OR ALL (“Insulin pump”). A search query on this topic found 17,532 scientific papers from 1974 to 2023. Results. The largest number of literary sources on the researched topic falls on 2014–2023. During this period, 10,531 works were published. In 2014, 728 papers were published, and in 2023 — 1,499 papers, which confirms the constant growth of interest and relevance in improving medical care for patients with diabetes worldwide. Since the statistics show a steady annual increase in the number of patients with diabetes, the search for effective methods of control and treatment of this nosology is extremely urgent. Conclusions. The implementation of medical information systems in diabetology has significant potential and can significantly improve the control and management of the disease, increase not only the duration, but also the quality of life of patients and optimize the work of medical personnel. [ABSTRACT FROM AUTHOR]

Expert systems (ESs) can be seen as a perspective method for risk analysis process automation, especially in the case of small- and medium-sized enterprises that lack internal security resources. Expert system practical applicability is limited by the fact that the creation of an expert system knowledge base requires a lot of manual work. External knowledge sources, such as attack trees, web pages, and ontologies, are already proven to be valuable sources for the automated creation of knowledge base rules, thus leading to more effective creation of specialized expert systems. This research proposes a new method of automated conversion of CVE data from the National Vulnerability Database (version CVSS 2) into the knowledge base of an expert system and flags CVE records that have higher risk due to already existing exploit tools. This manuscript also contains a description of the method for implementing software and a practical evaluation of conversion results. The uniqueness of the proposed method is incorporation of the records included in the Cybersecurity and Infrastructure Security Agency (CISA) Known Exploited Vulnerabilities Catalog. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

Currently, there is a need to improve the education system and develop interdisciplinary research at all levels of education, from school to postgraduate education. The introduction of interdisciplinary connections contributes to the formation of a holistic understanding of natural phenomena and the connections between them. Thus, this knowledge becomes more meaningful and applicable in practice. This article proposes a conceptual model of the content of education in the form of a thesaurus and ontology. The use of these models will allow you to adaptively select and systematize educational information. The article also discusses the possibilities and experience of using ontological modeling and engineering for the conceptual description of school and higher education. In addition, the article discusses the development of an ontological model of the process of expanding teachers' competencies with the integration of science, technology, engineering and mathematics (STEM) education. The use of ontological engineering methods will improve the quality of teacher education through the semantic description of knowledge in the subject area and the use of interdisciplinary and STEM approaches in the educational process. [ABSTRACT FROM AUTHOR]

Against the backdrop of swiftly changing industrial environments, this study aims to investigate the influence of technology convergence on the sustainable innovation of manufacturing enterprises. The purpose of this research is to determine the future competitiveness and expansion potential of industries by evaluating the impact of technological convergence on innovation performance, which serves as a significant metric for assessing the sustainability of corporate innovation practices. Specifically, the relationships among three characteristics of technological convergence and enterprise innovation performance—betweenness, closeness, and clustering—are analyzed. Using the financial, property, and patent data of listed companies in China's automotive manufacturing industry, an empirical study is conducted using a negative binomial regression model. Enhancing all three technology convergence characteristics is found to be conducive to enhancing sustainable innovation. Meanwhile, the corporate knowledge base plays a mediating role in which the effect of knowledge base width on clustering technology integration is more strongly mediated by the effect of knowledge base depth on approaching technology integration. The results of this study are useful for policymakers, corporate strategists, and innovation managers who are looking to enhance sustainable innovation practices within their organizations. By understanding the critical roles of betweenness, closeness, and clustering in technological convergence, stakeholders can better position their firms to leverage these attributes for improved innovation performance and competitive advantage. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In the realm of Wireless Sensor Networks (WSN), effective data dissemination is vital for applications like traffic alerts, necessitating innovative solutions to tackle challenges such as broadcast storms. OBJECTIVES: This paper proposes a pioneering framework that leverages probabilistic data aggregation to optimize communication efficiency and minimize redundancy. METHODS: The proposed adaptable system extracts valuable insights from the knowledge base, enabling dynamic route adjustments based on application-specific criteria. Through simulations addressing bandwidth limitations and local broadcast issues, we establish a robust WSN-based traffic information system. RESULTS: By employing primal-dual decomposition, the proposed approach identifies optimal packet aggregation probabilities and durations, resulting in reduced energy consumption while meeting latency requirements. CONCLUSION: The efficacy of proposed method is demonstrated across various traffic and topology scenarios, affirming that probabilistic data aggregation effectively mitigates the local broadcast problem, ultimately leading to decreased bandwidth demands. [ABSTRACT FROM AUTHOR]

Call graphs facilitate various tasks in software engineering. However, for the dynamic language Python, the complex language features and external library dependencies pose enormous challenges for building the call graphs of real projects. Some program analysis techniques used for call graph construction in other languages are impractical for Python. In this paper, we present STAR, a practical technique for the construction of Python static call graphs. We reformulate call graph construction as an entity identification task. STAR leverages inter-module summary and cross-project dependencies to construct a fine-grained entity knowledge base to identify the possible nodes and edges of the call graph in the code, and then construct the call graph. Our evaluation of three benchmarks shows that (1) STAR improves recall in three benchmarks compared to three baseline tools. Especially, STAR improves the recall of reachable nodes and reachable edges compared with the state-of-the-art tool by 11.3% and 9.8%, respectively; (2) STAR achieves comparable performance as three baseline tools in execution time and memory usage and is more efficient in large projects; (3) STAR can be effectively used for the task of detecting vulnerability propagation with real-world cases. We expect our results will attract more exploration of practical methods and improve the application of Python call graphs. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment analysis (ABSA) is a pivotal task within the domain of neurorobotics, contributing to the comprehension of fine-grained textual emotions. Despite the extensive research undertaken on ABSA, the limited availability of training data remains a significant obstacle that hinders the performance of previous studies. Moreover, previous works have predominantly focused on concatenating semantic and syntactic features to predict sentiment polarity, which inadvertently severed the intrinsic connection. Several studies have attempted to utilize multi-layer graph convolution for the purpose of extracting syntactic characteristics. However, this approach has encountered the issue of gradient explosion. This paper investigates the possibilities of leveraging ChatGPT for aspect-level text augmentation. Furthermore, we introduce an improved gated attention mechanism specifically designed for graph convolutional networks to mitigates the problem of gradient explosion. By enriching the features of the dependency graph with a sentiment knowledge base, we strengthen the relationship between aspect words and the polarity of the contextual sentiment. It is worth mentioning that we employ cross-fusion to effectively integrate textual semantic and syntactic features. The experimental results substantiate the superiority of our model over the baseline models in terms of performance. [ABSTRACT FROM AUTHOR]

Feed efficiency (FE) is essential for pig production, has been reported to be partially explained by gut microbiota. Despite an extensive body of research literature to this topic, studies regarding the regulation of feed efficiency by gut microbiota remain fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Meanwhile, structured databases for microbiota analysis are available, yet they often lack a comprehensive understanding of the associated biological processes. Therefore, we have devised an approach to construct a comprehensive knowledge graph by combining unstructured textual intelligence with structured database information and applied it to investigate the relationship between pig gut microbes and FE. Firstly, we created the pgmReading knowledge base and the domain ontology of pig gut microbiota by annotating, extracting, and integrating semantic information from 157 scientific publications. Secondly, we created the pgmPubtator by utilizing PubTator to expand the semantic information related to microbiota. Thirdly, we created the pgmDatabase by mapping and combining the ADDAGMA, gutMGene, and KEGG databases based on the ontology. These three knowledge bases were integrated to form the Pig Gut Microbial Knowledge Graph (PGMKG). Additionally, we created five biological query cases to validate the performance of PGMKG. These cases not only allow us to identify microbes with the most significant impact on FE but also provide insights into the metabolites produced by these microbes and the associated metabolic pathways. This study introduces PGMKG, mapping key microbes in pig feed efficiency and guiding microbiota-targeted optimization. [ABSTRACT FROM AUTHOR]

Cleaning control based on changes in cleaning loss rate and impurity rate has emerged as a hot topic in the research on intelligent control for rice and wheat combined harvesters. However, numerous operation parameters can lead to deviations beyond the normal range of cleaning loss rate and impurity rate. The impact of cleaning control parameters in rice and wheat combined harvesters on cleaning loss rate and impurity rate often tends to be contradictory. How to combine different contradictory cleaning control strategies to get a more widely used and better cleaning control strategy is a hot issue in the current cleaning control research. In this paper, the granularity of quotient space is introduced into the cleaning control based on operation parameters, and a cleaning control model based on granularity synthesis theory is proposed. This method first constructs a knowledge base for intelligent control of cleaning tailored to the cleaning loss rate and impurity rate using production rule representation, and considers that these control strategies constitute different quotient spaces, and then organizes these quotient spaces according to granularity synthesis theory to get the cleaning control strategy. The experimental results verify that the validity of the cleaning control based on granular computing is better than fuzzy control. [ABSTRACT FROM AUTHOR]

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan‐cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge‐mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well‐functioning, content‐rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field. [ABSTRACT FROM AUTHOR]