Search Results (3,713)

Digital Twin (DT) technology has emerged as a transformative tool in urban flood risk management (UFRM), enabling real-time data integration, predictive modeling, and decision support. This systematic review synthesizes existing literature to evaluate the scientific impact, technological advancements, and practical applications of DTs in UFRM. Using the PRISMA 2020 framework, we retrieved 1085 records (Scopus = 85; Web of Science = 1000), merged and deduplicated them using DOI and fuzzy-matched titles, screened titles/abstracts, and assessed full texts. This process yielded 85 unique peer-reviewed studies published between 2018 and 2025. Key findings highlight the role of remote sensing (e.g., satellite imagery, IoT sensors) in enhancing DT accuracy, the integration of machine learning for predictive analytics, and case studies demonstrating reduced flood response times by up to 40%. Challenges such as data interoperability and computational demands are discussed, alongside future directions for scalable, AI-driven DT frameworks. This review identifies key technical and governance challenges while recommending the development of modular, AI-driven DT frameworks, particularly tailored for resource-constrained regions. Full article

A learning management system (LMS) plays a crucial role in supporting students’ educational activities by centralized platforms for course delivery, communication, and student support. Recently, many universities have integrated chatbots into their LMS to assist students with various inquiries and tasks. However, existing chatbots often necessitate human interventions to manually respond to complex queries, resulting in limited scalability and efficiency. In this paper, we present a memory-augmented large language model (LLM) framework that enhances the reasoning and contextual continuity of LMS-based chatbots. The proposed framework first embeds user queries and retrieves semantically relevant entries from various LMS resources, including instructional documents and academic frequently asked questions. Retrieved entries are then filtered through a two-stage confidence filtering process that combines similarity thresholds and LLM-based semantic validation. Validated information, along with user queries, is processed by LLM for response generation. To maintain coherence in multi-turn interactions, the chatbot incorporates short-term, long-term, and temporal event memories, which track conversational flow and personalize responses based on user-specific information, such as recent activity history and individual preferences. To evaluate response quality, we employed a multi-layered evaluation strategy combining BERTScore-based quantitative measurement, an LLM-as-a-Judge approach for automated semantic assessment, and a user study under multi-turn scenarios. The evaluation results consistently confirm that the proposed framework improves the consistency, clarity, and usefulness of the responses. These findings highlight the potential of memory-augmented LLMs for scalable and intelligent learning support within university environments. Full article

Late bilingual speakers immersed in a second language (L2) environment often experience the non-pathological attrition of their first language (L1), exhibiting selective and reversible changes in L1 processing and production. While attrition research has largely focused on long-term residents in anglophone countries, examining changes primarily within a single L1 domain, the present study employs a novel experimental design to investigate L1 attrition, alongside L2 acquisition, across three domains (i.e., the lexicon, syntax–pragmatics interface, and prosody) in two groups of L1-English L2-Italian late bilinguals: long-term residents in Italy vs. university students in the UK. A total of 112 participants completed online tasks assessing lexical retrieval, anaphora resolution, and sentence stress patterns in both languages. First, both bilingual groups showed comparable levels of semantic interference in lexical retrieval. Second, at the syntax–pragmatics interface, only residents in Italy showed signs of L1 attrition in real-time processing of anaphora, while resolution preferences were similar between groups; in the L2, both bilingual groups demonstrated target-like preferences, despite some slowdown in processing. Third, while both groups showed some evidence of target-like L2 prosody, with residents in Italy matching L1-Italian sentence stress patterns closely, prosodic attrition was only reported for residents in Italy in exploratory analyses. Overall, this study supports the notion of L1 attrition as a natural consequence of bilingualism—one that is domain- and experience-dependent, unfolds along a continuum, and involves a complex (and possibly inverse) relationship between L1 and L2 performance that warrants further investigation. Full article

Efficient and rational utilization of mineral resources significantly impacts economic and technological development. Image segmentation is a pivotal process in ore sorting, as its results directly affect the accuracy of mineral classification. Traditional segmentation methods often fail to meet the requirements for noise suppression, segmentation precision, and robustness in ore sorting. To address these issues, we propose an ore image segmentation method based on concavity matching via region retrieval, which comprises a contour approximation module, a concavity matching module, and a segmentation detection module. It introduces the concepts of single-contour, multi-contour, and segmentation regions in ore images, offering tailored segmentation approaches for varying adhesion forms and quantities. A significant contribution of this study lies in the contour approximation module, which simplifies the edge information of ore images via curve fitting, effectively removing the influence of edge noise points. The concavity matching module restricts candidate areas for matching concavity points through the construction of search regions, significantly improving matching accuracy. Finally, paired concavity points are connected to completing the segmentation process. Experimental comparisons using X-ray images of tungsten ores demonstrate that the proposed method can effectively suppress noise-induced concavity interference, achieving a noise reduction efficiency of 94.77% and a concavity region search accuracy of 93.60%, thus meeting the precision requirements for segmenting X-ray ore images. Given its high efficiency and accuracy, industrial sectors involved in mineral processing are recommended to incorporate this segmentation method into intelligent ore sorting equipment upgrading and renovation projects, enhancing the overall efficiency of mineral resource sorting and promoting the sustainable development of the mineral industry. Full article

This study tries to make some improvements in the order picking operations by offering a novel mathematical model and efficient solution algorithm. Accordingly, the order picking policies are examined to allow for picking more orders by reducing the collection time/distance of order pickers. Batching orders for the pick are included in the order picking process as it could enable the order picker to collect more orders. Since the most labor-intensive movement in the order picking function in a high-level shelf layout is the retrieval of products from upper shelves and placing them onto the collection vehicle in the picker-to-part system, the use of drones is preferred to eliminate this costly movement. Drones assist humans in the order picking process by retrieving products from upper levels, thus reducing the order picking time. Here, a Vehicle Routing Problem (VRP) is formulated to deal with drone routing which is then solved based on the Order Picking Problem (OPP) framework. Consequently, an integrated OPP involving both order pickers and drones is addressed and formulated using a Mixed-Integer Linear Programming (MILP) model. To cope with the complexity of the problem, an Adaptive Genetic Algorithm (AGA) is designed which is able to yield superior results compared to the classical Genetic Algorithm (GA). Finally, a sensitivity analysis is performed to assess the behavior of the model against real-world fluctuations. The main reason for this research is to speed up the order picking process in warehouses by taking advantage of the tools brought by the technology age. According to the research results, when the results of the drone-assisted order picking process are compared to the order picking process without drone support, an improvement of 29.68% is observed. The theoretical contribution of this work is that it initially mathematically defines the drone-aided OPP in the literature and proposes a solution with the help of the AGA. As a practical contribution, it provides a solution with the capacity to reduce operational costs by accelerating the order picking operation in warehouses and a practical optimization framework for logistics managers. In addition, warehouse managers, senior company managers, and researchers working on order picking processes can benefit from this study. Full article

The increasing number of catastrophic events has relentlessly disrupted production and distribution processes across the globe. To address this issue, the current study developed a research model that combines factors such as human capital, relational capital, structural capital, HR practices, risk management capability, and artificial intelligence to investigate logistic firm resilience capability. The research design was based on quantitative methods. Data were collected from logistic managers. A total of 213 questionnaires were retrieved for the research survey. Statistical findings revealed that human capital, relational capital, structural capital, HR practices, and artificial intelligence explained $R^2$ 86.5% of the variance in logistic firm resilience capability. Nevertheless, the relationship between risk management and resilience capabilities was found to be insignificant. On the other hand, logistic service quality and firm resilience capability explained $R^2$ 79.5% of the variance in logistic firm performance. Practically, this study suggests that adequate logistic service quality, appropriate intellectual capital, good HR practices, and the deployment of artificial intelligence in logistic operations could boost firm resilience capability, resulting in better performance during catastrophic events. The present study is original in that it investigated logistic firms’ resilience capability with intellectual capital, HR practices, and artificial intelligence. Another unique aspect of this study is that it established the moderating impact of logistic service quality on the relationship between logistic firm resilience capability and firm performance. Full article

Smoking tobacco is a prominent activity in Cameroon, with each region holding different views on the devices used for smoking. In Bamum, these vessels are called kiis. Many of these pipes, or kiis, have been removed from the kingdom and displayed without proper contextual information in Western institutions since the 1920s. This paper highlights discrepancies in academic pursuits regarding the kii and their decontextualized displays, while also providing ethical guidelines for their future displays. Understanding the intended purpose and cultural significance of a kii allows for the process of restitution in the form of ethical display to take place. Using the Heritage Context Retrieval Analysis (HeCRA) method, the research aim to explore the cultural origins of the kii in the GWU collection, retrieve its cultural context, critique the prevalent cabinet of curiosities display format used in displaying them in museums, and propose ethical frameworks for handling such devices, which are both utilitarian and culturally charged in 21st-century museums. This paper uncovers the true identity of a brass kii and dismantles the cabinet of curiosities and the alignment of African tangible heritage to oddities. The goal is to instigate a new approach to approaching such cultural objects by invoking their original spiritual and cultural symbolism in exhibitions outside of Bamum. Full article

The exponential growth of multimedia communication necessitates advanced techniques for secure data transmission. This paper details a new data hiding method centered on a predictive voting mechanism that leverages neighboring pixels to estimate a pixel’s value. Secret data are concealed within these predictions via a purpose-built lookup table, and the retrieval process involves re-estimating the predicted pixels and applying an inverse mapping function. Experimental results demonstrate that the proposed method achieves an embedding capacity of up to 686,874 bits, significantly outperforming previous approaches while maintaining reliable data recovery. Compared with existing schemes, our approach offers improved performance in terms of both embedding capacity and extraction accuracy, making it an effective solution for robust multimedia steganography. Full article

The volume and diversity of digital information have led to a growing reliance on Machine Learning (ML) techniques, such as Natural Language Processing (NLP), for interpreting and accessing appropriate data. While vector and graph embeddings represent data for similarity tasks, current state-of-the-art pipelines lack guaranteed explainability, failing to accurately determine similarity for given full texts. These considerations can also be applied to classifiers exploiting generative language models with logical prompts, which fail to correctly distinguish between logical implication, indifference, and inconsistency, despite being explicitly trained to recognise the first two classes. We present a novel pipeline designed for hybrid explainability to address this. Our methodology combines graphs and logic to produce First-Order Logic (FOL) representations, creating machine- and human-readable representations through Montague Grammar (MG). The preliminary results indicate the effectiveness of this approach in accurately capturing full text similarity. To the best of our knowledge, this is the first approach to differentiate between implication, inconsistency, and indifference for text classification tasks. To address the limitations of existing approaches, we use three self-contained datasets annotated for the former classification task to determine the suitability of these approaches in capturing sentence structure equivalence, logical connectives, and spatiotemporal reasoning. We also use these data to compare the proposed method with language models pre-trained for detecting sentence entailment. The results show that the proposed method outperforms state-of-the-art models, indicating that natural language understanding cannot be easily generalised by training over extensive document corpora. This work offers a step toward more transparent and reliable Information Retrieval (IR) from extensive textual data. Full article

The development of medical question-answering systems (QASs) encounters substantial challenges due to the complexities of medical terminologies and the lack of reliable datasets. The shortcomings of traditional artificial intelligence (AI) driven QAS lead to generating outcomes with a higher rate of hallucinations. In order to overcome these limitations, there is a demand for a reliable QAS to understand and process complex medical queries and validate the quality and relevance of its outcomes. In this study, we develop a medical QAS by integrating SPARQL, retrieval-augmented generation (RAG), and generative pre-trained transformer (GPT)-Neo models. Using this strategy, we generate a synthetic dataset to train and validate the proposed model, addressing the limitations of the existing QASs. The proposed QAS was generalized on the MEDQA dataset. The findings revealed that the model achieves a generalization accuracy of 87.26% with a minimal hallucination rate of 0.16. The model outperformed the existing models by leveraging deep learning techniques to handle complex medical queries. The dynamic responsive capability of the proposed model enables it to maintain the accuracy of medical information in a rapidly evolving healthcare environment. Employing advanced hallucination reduction and query refinement techniques can fine-tune the model’s performance. Full article

This review explores the evolution and current state of machine learning (ML) and artificial intelligence (AI) applications in direct energy deposition (DED) and wire arc additive manufacturing (WAAM) processes. A Python-based automated search script was developed to systematically retrieve relevant literature using the Crossref API, yielding around 370 papers published between 2010 and July 2025. The study identifies significant growth in ML-related DED research starting in 2020, with increasing adoption of advanced techniques such as deep learning, fuzzy logic, and hybrid physics-informed models. A year-by-year trend analysis is presented, and a comprehensive categorization of the literature is provided to highlight dominant application areas, including process optimization, real-time monitoring, defect detection, and melt pool prediction. Key challenges, such as limited closed-loop control, lack of generalization across systems, and insufficient modeling of deposition-location effects, are discussed. Finally, future research directions are outlined, emphasizing the need for integrated thermo-mechanical models, uncertainty quantification, and adaptive control strategies. This review serves as a resource for researchers aiming to advance intelligent control and predictive modeling in DED-based additive manufacturing. Full article

Research articles are valuable resources for Information Retrieval and Natural Language Processing (NLP) tasks, offering opportunities to analyze key components of scholarly content. This study investigates the presence of methodological terminology in psychology research over the past 30 years (1995–2024) by applying a novel NLP and Machine Learning pipeline to a large corpus of 85,452 abstracts, as well as the extent to which this terminology forms distinct thematic groupings. Combining glossary-based extraction, contextualized language model embeddings, and dual-mode clustering, this study offers a scalable framework for the exploration of methodological transparency in scientific text via deep semantic structures. A curated glossary of 365 method-related keywords served as a gold-standard reference for term identification, using direct and fuzzy string matching. Retrieved terms were encoded with SciBERT, averaging embeddings across contextual occurrences to produce unified vectors. These vectors were clustered using unsupervised and weighted unsupervised approaches, yielding six and ten clusters, respectively. Cluster composition was analyzed using weighted statistical measures to assess term importance within and across groups. A total of 78.16% of the examined abstracts contained glossary terms, with an average of 1.8 term per abstract, highlighting an increasing presence of methodological terminology in psychology and reflecting a shift toward greater transparency in research reporting. This work goes beyond the use of static vectors by incorporating contextual understanding in the examination of methodological terminology, while offering a scalable and generalizable approach to semantic analysis in scientific texts, with implications for meta-research, domain-specific lexicon development, and automated scientific knowledge discovery. Full article

The growth of e-government has rendered automated element extraction from official documents a critical bottleneck for administrative efficiency. The core challenge lies in unifying deep semantic understanding with the structured domain knowledge required to interpret complex formats and specialized terminology. To address the limitations of existing methods, we propose a hybrid framework. Our approach leverages a BERT-CRF model for robust sequence labeling, a knowledge graph (KG)-driven retrieval system to ground the model in verifiable facts, and a large language model (LLM) as a reasoning engine to resolve ambiguities and identify complex relationships. Validated on the DovDoc-CN dataset, our framework achieves a macro-average F1 score of 0.850, outperforming the BiLSTM-CRF baseline by 2.41 percentage points, and demonstrates high consistency, with a weighted F1 score of 0.984. The low standard deviation in the validation set further indicates the model’s stable performance across different subsets. These results confirm that our integrated approach provides an efficient and reliable solution for intelligent document processing, effectively handling the format diversity and specialized knowledge characteristic of government documents. Full article

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, with pediatric ALL having a ~90 percent cure rate, while the adult cure rate is considerably lower. B-cell acute lymphoblastic leukemia (B-ALL) is the most common subtype of ALL and is generally treated through a variety of chemotherapy drugs that can cause undesired side effects, adverse events, or other complications. Consequently, there is a need for improved understanding of the shared gene expression profiles and underlying molecular mechanisms shared among various B-ALL subtypes. In this study, 259 publicly available RNA-sequencing samples were evaluated and retrieved from the NCBI Gene Expression Omnibus (GEO) database and then pre-processed using a robust computational workflow. Differential gene expression, pathway enrichment, marker prediction, and drug repurposing analyses were then performed to facilitate a better mechanistic understanding of disease. We found both previously identified as well as novel differentially expressed genes. Specifically, we observed upregulation in the HIST2H2AA3, EPHA7, and MPR1 genes; while downregulation was observed for the IGHA1, ANGPTL1, and CHAD genes. We identified multiple pathways, including “Integrins in Angiogenesis”, to be significantly affected in B-ALL. We then used these significant pathways to predict and rank 306 existing therapeutic targets that could potentially be repurposed for B-ALL, including three that have not been evaluated in human clinical trials. Using a tree-based classification algorithm, we also predicted ADAM28 as a possible mechanistic marker. The results of this study have potential implications for patients who have been diagnosed with B-ALL by providing improved mechanistic understanding and information on possible diagnostics and repurposed therapeutics for B-ALL. Full article

Solving math word problems automatically is a critical task in the field of natural language processing. Due to the insufficient size of existing MWP datasets, recent models have reached a performance bottleneck. Large-scale and high-quality training examples are crucial for training a robust math solver, but existing high-quality datasets have limited scale, and annotating or synthesizing vast MWPs explicitly is highly expensive. To address these issues, we propose a novel hidden space-based retrieval augmentation self-distillation method, named RASD, to improve the mathematical reasoning performance of MWP solvers with semantic representation augmentation and self-distillation learning. RASD enhances problem representations by retrieving and merging similar ones. It then inputs both the original and augmented representations into the decoder for solution reasoning. A self-distillation objective is used to maintain reasoning consistency between them. Extensive experiments on five popular math word problem-solving benchmarks, including MAWPS, Math23K, ASDiv-A, SVAMP, and GeoQA, show the effectiveness and universality of our RASD on improving the math reasoning ability of multiple popular baseline solvers. Full article