Search Results (111)

In cybersecurity, constructing an accurate knowledge graph is vital for discovering key entities and relationships in security incidents buried in vast unstructured threat reports. Traditional knowledge-graph construction pipelines based on handcrafted rules or conventional machine learning models falter when the data scale and linguistic variety grow. GraphRAG, a retrieval-augmented generation (RAG) framework that splits documents into fixed-length chunks and then retrieves the most relevant ones for generation, offers a scalable alternative yet still suffers from fragmentation and semantic gaps that erode graph integrity. To resolve these issues, this paper proposes SC-LKM, a cybersecurity knowledge-graph construction method that couples the GraphRAG backbone with hierarchical semantic chunking. SC-LKM applies semantic chunking to build a cybersecurity knowledge graph that avoids the fragmentation and inconsistency seen in prior work. The semantic chunking method first respects the native document hierarchy and then refines boundaries with topic similarity and named-entity continuity, maintaining logical coherence while limiting information loss during the fine-grained processing of unstructured text. SC-LKM further integrates the semantic comprehension capacity of Qwen2.5-14B-Instruct, markedly boosting extraction accuracy and reasoning quality. Experimental results show that SC-LKM surpasses baseline systems in entity-recognition coverage, topology density, and semantic consistency. Full article

This article presents an interpretable framework for customer segmentation and churn risk detection, integrating fuzzy clustering, explainable AI (XAI), and strategic scoring. The process begins with Fuzzy C-Means (FCM) applied to normalized RFM indicators (Recency, Frequency, Monetary), which were then mapped to a 2-tuple linguistic scale to enhance semantic interpretability. Cluster memberships and centroids were analyzed to identify distinct behavioral patterns. An XGBoost classifier was trained to validate the coherence of the fuzzy segments, while SHAP and LIME provided global and local explanations for the classification decisions. Following segmentation, an AHP-based strategic score was computed for each customer, using weights derived from pairwise comparisons reflecting organizational priorities. These scores were also translated into the 2-tuple domain, reinforcing interpretability. The model then identified customers at risk of disengagement, defined by a combination of low Recency, high Frequency and Monetary values, and a low AHP score. Based on Recency thresholds, customers are classified as Active, Latent, or Probable Churn. A second XGBoost model was applied to predict this risk level, with SHAP used to explain its predictive behavior. Overall, the proposed framework integrated fuzzy logic, semantic representation, and explainable AI to support actionable, transparent, and human-centered customer analytics. Full article

To address potential risks during the construction process, improve construction quality and engineering safety, this paper constructs a construction safety risk analysis model for concrete gravity dams in cold regions based on fuzzy VIKOR-LEC. Firstly, an expert team employs linguistic variables to evaluate the likelihood of accidents (L), the frequency of personnel exposure to hazardous environments (E), and the consequences of accidents (C) for various risk factors in the LEC model. Secondly, the fuzzy analytic hierarchy process (FAHP) and maximum deviation method were used to construct a risk factor weight analysis matrix and find subjective and objective weights, respectively, to obtain the comprehensive weights of risk factors. Thirdly, VlseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) is introduced to improve the traditional LEC model and is used to calculate the risk priority number. Finally, in order to further verify the validity of the model, this paper selects the example of Linhai Reservoir dam in Heilongjiang Province to analyze the management of the construction safety risk. The research results may provide a scientific basis for the safety management of gravity dam construction projects in cold areas, and help to improve the level of project management and reduce construction risks. Full article

The construction industry plays a key role in economic development but continues to face challenges in promoting employee well-being, particularly mental health and social sustainability. While existing decision-making tools emphasize environmental and economic factors, the social dimension remains largely overlooked, creating a significant gap in both research and practice. To address this, the study develops a decision support tool (DST) to help construction organizations prioritize strategic investments that enhance employee social sustainability. The tool is based on a hybrid multi-criteria decision-making framework, combining the Analytical Hierarchy Process (AHP) with Fuzzy MOORA to integrate both quantitative and qualitative assessments. A literature review, along with findings from a previous empirical study, identified 27 validated criteria, grouped into seven core sustainability alternatives. Additionally, five decision criteria (cost, risk, compatibility, return on investment, and difficulty) were refined through expert interviews. The DST was implemented as a modular Excel-based tool allowing users to input data, conduct pairwise comparisons, evaluate alternatives using linguistic scales, and generate a final ranking through defuzzification. A case study in a private construction company showed Training and Development and Work Environment as top priorities. An online expert focus group confirmed the DST’s clarity, usability, and strategic relevance. By addressing the often-neglected social pillar of sustainability, this tool offers a practical and transparent framework to support decision-making, ultimately enhancing employee well-being and organizational performance in the construction sector. Full article

Intellectual preferences often align with the broader concept of standardization. The centralizing tendency observed in the sciences mirrors the patterns seen in linguistic standardization, such as the establishment of standard dialects in diverse speech communities. In both cases, there is a deliberate disregard for the complexities of the “lower” systems within the hierarchy and an exaggerated belief in the purity of the dominant system. The process of language standardization involves minimizing linguistic variation, often leading to the marginalization of non-standard varieties and reinforcing social hierarchies by privileging certain forms of language, which can restrict access to opportunities and institutional authority. The hierarchical tendencies observed in both scientific disciplines and linguistic standardization reflect a broader intellectual preference for centralized, “pure” systems, often at the expense of diversity and complexity. This paper explores the relationship between linguistic and scientific standardization, highlighting their influence on knowledge, authority, and social structures. Focusing on the global use of Greco-Latin scientific terminology, it examines both the practical advantages and cultural implications of standardized scientific language. While proponents emphasize its unifying role, critics argue it threatens linguistic purity and cultural identity. Through historical and contemporary debates, the paper argues that standardization serves as both a tool for communication and a contested space reflecting ideological tensions about language, culture, and knowledge. Topics include the politics of language standardization, the globalization of scientific vocabulary, debates on the interlingual lexicon, and the conflict between global communication and Arabic language preservation. Full article

To address the challenges of assessing information security risks in smart energy systems, this study proposes a multi-attribute decision support method based on interval type-2 fuzzy numbers (IT2TrFN). First, expert questionnaires were designed to gather insights from eight specialists in the fields of smart energy and safety engineering. Linguistic terms associated with IT2TrFN were employed to evaluate indicators, converting expert judgments into fuzzy numerical values while ensuring data reliability through consistency measurements. Subsequently, a decision hierarchy structure and an expert weight allocation model were developed. By utilizing the score and accuracy functions of IT2TrFN, the study determined positive and negative ideal solutions to rank and prioritize the evaluation criteria. Key influencing factors identified include the rate of excessive initial investment, regulatory stringency, information security standards, environmental pollution pressure, and incident response timeliness. The overall risk index was calculated as 0.5839, indicating a moderate level of information security risk in the evaluated region. To validate the robustness of the model, sensitivity analyses were conducted by varying IT2FWA (Weighted aggregated operator) and IT2FGA (Weighted geometric operator) operator selections and adjusting weight coefficients. The results reveal that key indicators exhibit high risk under different scenarios. This method provides an innovative tool for the scientific evaluation of information security risks in smart energy systems, laying a solid theoretical foundation for broader regional applications and the expansion of assessment criteria. Full article

Academic laboratories operate in diverse fields. However, they expose individuals to occupational risks. To ensure social and economic sustainability, organizations must assess these risks. Accident prevention reduces injuries and financial losses. This study aims to structure an occupational risk assessment process in a university chemical laboratory in Brazil using the fuzzy analytic hierarchy process (FAHP). The methodology consists of identifying risks and applying the FAHP with linguistic variables to evaluate and prioritize them. Fifteen hazard sources were identified. In the risk assessment phase, the “Chemical Risk” criterion was given the highest priority, accounting for 54%, followed by “Accident Risk” at 26%, “Physical Risk” at 13%, and “Ergonomic risk” at 7%. This study contributes to applying a novel scientific method that enables low-cost risk assessment, reducing the need for significant investments in technology or specialized consultancy services. Furthermore, the research suggests applying this technique to different economic sectors, broadening the applicability of FAHP in occupational risk assessment. Full article

This research investigates linguistic hybridization in a commercial corridor of Armenia, Colombia, focusing on the usage of Spanish and English in public signage, particularly business names. Utilizing a quantitative methodology, we conducted a statistical analysis employing Chi-square tests to explore the relationship between symbolic language choice and variables such as location and type of establishment. The results demonstrated a significant association between location and language choice (Χ² = 39.353, p < .001), revealing that commercial zones with high tourist traffic exhibited a pronounced preference for English (46.55%), reflecting branding strategies aimed at attracting a younger, cosmopolitan audience. Conversely, traditional sectors such as health services (74.24% in Spanish) and religious institutions (80% in Spanish) predominantly utilized Spanish, emphasizing the community’s need for accessible communication. Additionally, establishments in the most commercial area highlighted the presence of hybrid names, indicating a blending of languages. Our findings suggest that the hybridization of English and Spanish serves as both a reflection and reinforcement of cultural identity and social hierarchies, emphasizing the role of linguistic capital in shaping social dynamics within the urban landscape of Armenia, Colombia. Full article

Pitahaya (Hylocereus megalanthus), commonly known as dragon fruit, is grown in tropical areas and has a promising future in the world market. At present, it is a crop developed by small-scale farmers. However, finding optimal areas for installing this crop is a major challenge. In this study, we evaluated the suitability of land for pitahaya cultivation in the department of Amazonas using integrated multi-criteria techniques such as geographic information systems (GISs) and remote sensing (RS). The analytic hierarchy process (AHP) method was used to select and rank the suitability criteria. The fuzzy-AHP (F-AHP) method was then applied to perform pairwise comparisons and determine the linguistic scaling of the requirements, and, using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), the requirements with the highest preference for land suitability were selected. The results reported that for pitahaya cultivation, the most important criterion was mean annual temperature (20.70%), followed by soil organic matter (11.8%), mean annual rainfall (9.50%), and proximity to roads (9.0%). The final suitability map indicated that 0.006% (2.39 km²) was very suitable, 4.60% (1661.97 km²) moderately suitable, 0.10% (34.65 km²) marginally suitable, and 95.30% (34,459.31 km²) of the study area was not suitable. Full article

Children’s consonant cluster productions in typical and atypical phonological development were investigated for different languages reporting developmental productions that are universal, language-specific, and/or child-specific. These patterns are often interpreted considering sonority hierarchy effects. Quantitative norms on developmental cluster productions are less prevalent in the literature cross-linguistically, as are investigations on the development of less frequent cluster types in the world’s languages, like those involving falling and level sonority two-member onsets. Our study contributes to these investigations, focusing on Greek-specific onsets: falling sonority obstruents [ft, xt], level sonority obstruents [fθ, fç, ðʝ, xθ, ɣð], and level sonority nasals [mɲ]. We present cross-sectional, longitudinal data from 90 monolingual children, aged 2;0–4;0, based on the word elicitation task, Phonological Assessment for Greek (PAel). As only [ft] 89%, [fç] 80%, [mɲ] 88% are acquired by 3;6–4;0, the data provide evidence that [ft, xt, fθ, xθ, ɣð] reduce to C2, [mɲ] reduces to C1, and [fç], [ðʝ] show the most variability in reduction/simplification patterns. Reduction patterns largely reflect individual cluster acquisition paths longitudinally; the relative reduction to a member changes with age, but the preference to the member does not, except for [ðʝ]. The data facilitate the establishment of quantitative markers for cluster development and qualitative interpretations in terms of featural and structural prominence, including a veiled sonority effect not previously reported in the literature. Full article

This article examines the complex linguistic phenomenon of Hebrew–Yiddish diglossia within Hasidic homiletic literature, particularly focussing on sermons from the nineteenth and twentieth centuries. While previous scholarship has emphasised Hebrew’s dominance in Hasidic written works, this study demonstrates how Yiddish has played a crucial role in preserving and transmitting Hasidic teachings. Through analysis of primary sources, three distinct models of Hebrew–Yiddish integration are identified: parallel texts in both languages within the same volume, limited Yiddish passages integrated within predominantly Hebrew texts, and a complete amalgamation where the languages become nearly inseparable. Analysis indicates that Hasidic authors and editors deliberately preserved Yiddish elements to maintain the authenticity of the tzaddik’s original oral teachings while adhering to Hebrew’s traditional status in religious literature. This linguistic practice elevated Eastern Yiddish’s cultural position concurrent with similar (but different) developments in Haskalah literature. Furthermore, the study demonstrates how Hasidic literature’s incorporation of spoken Yiddish into sacred texts contributed to the language’s legitimisation as a medium for religious discourse. This examination offers new perspectives on linguistic hierarchies in religious Jewish texts and illuminates how Hasidic literature developed innovative solutions to balance authenticity and tradition in religious writing. Full article

The rapid growth of the internet and social media has made electronic word of mouth (eWOM) a key element of modern marketing. In the hospitality industry, nowadays, effective eWOM management is essential for developing impactful strategies and fostering customer satisfaction. This paper introduces an enhanced approach to strategic customer base management based on online reviews by extending the Recency, Frequency, and Monetary (RFM) model with three novel dimensions, the Helpfulness, Promoter Score, and Stability of the customer, thereby forming the RFHPS model. It also includes the 2-tuple linguistic model, one of the most popular computing with words models, to improve precision in the RFHPS score’s computation and the findings’ interpretability. Using K-means clustering, customers are segmented across these five dimensions. The data on luxury hotels in Spain gathered from TripAdvisor demonstrate the model’s applicability. By integrating this framework into customer relationship management systems, managers can tailor marketing strategies for distinct segments, facilitating deeper customer understanding and bolstering eWOM generation. Full article

With the advent of digitization, digital twin technology is gradually becoming one of the core technologies of the Industry 4.0 era, highlighting the increasing importance of digital twin project management. Despite its potential, DT projects face significant risks during implementation, stemming from technical, managerial, and operational complexities. To address these challenges, this study proposes an improved failure mode and effect analysis (FMEA) framework by integrating double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This framework converts qualitative assessments into quantitative metrics and calculates weights using a hybrid approach, enabling more precise risk prioritisation. Application of the model to an automotive manufacturing company’s DT project identified key risks, particularly in the iteration and upgrade phase, emphasising the importance of cross-departmental collaboration and robust digital infrastructure. The proposed model provides a systematic framework for enterprises to assess and mitigate risks, ensuring the successful deployment of DT projects. Full article

Within the realm of graphic design sustainability, selecting appropriate solutions has become a crucial strategic decision for organizations aiming to optimize their operations. This paper presents a novel hybrid multi-criteria decision-making (MCDM) approach, integrating a fuzzy analytical hierarchy process (FAHP) and fuzzy measurement alternatives and ranking according to compromise solution (FMARCOS). Evaluation criteria for graphic design sustainability are determined through consultation with experts, with their judgments expressed using linguistic terms based on fuzzy numbers. Criteria weights are calculated using FAHP, and the ranking and selection of the optimal potential solution are determined using FMARCOS. Subsequently, sensitivity analysis of the criteria weights is conducted to validate the results. Findings indicate that the integrated FAHP and FMARCOS model provides a robust and adaptable assessment framework for graphic design sustainability, enabling companies to navigate complexities strategically and effectively. The key contribution of this research is its emphasis on a systematic and objective model, offering practical insights relevant to the industry. It also serves as a valuable benchmark for future research in similar fields. Full article

In the expanding landscape of biomedical literature, numerous latent associations outlined in scholarly papers await discovery and integration into biomedical databases. Biomedical Natural Language Processing (NLP) research focuses on automating knowledge extraction and mining from this literature, particularly emphasizing the essential task of Relation Extraction (RE). However, existing models have limitations, mainly in their applicability to partial datasets for RE tasks. Moreover, conventional models often treat RE as a binary classification challenge, which proves suboptimal given the diverse relationships, including intricate ones like similarity and hierarchy, present in the RE dataset. These limitations are exacerbated by the models’ inability to capture word-level positional nuances and sentence-level language features. In response to these challenges, we present a novel RE model called BicapBert. This model integrates neural networks and capsule networks, enhancing them with hybrid knowledge graph embeddings to extract relevant features. BicapBert utilizes PubMedBERT and capsule networks to extract word-level positional and sentence-level language features. It further captures knowledge features from a biomedical knowledge graph, integrating them with the aforementioned linguistic features. The amalgamated information is then input into a multi-layer perceptron, culminating in results derived through a softmax classifier. Experimental evaluations on three extensive RE task datasets showcase the state-of-the-art performance of our proposed model. Additionally, we validate the model’s efficacy on three randomly selected biomedical datasets for various tasks, further affirming its superiority. Full article