Fine-grained Multimodal Named-Entity Recognition and Grounding (FMNERG) aims to extract entity–type–object triples from image–text pairs. However, existing methods still struggle with two major challenges: distinguishing semanticall...
Conventional independently trained models for distributed photovoltaic (DPV) power forecasting at the feeder, main-transformer, and regional-grid levels do not explicitly reuse spatiotemporal representations across the distribution-network hierarchy....
Distantly supervised relation extraction (DSRE), first used to address the limitations of manually annotated data via automatically annotating the data with triplet facts, is prone to issues such as mislabeled annotations due to the interference of n...
In this paper, we propose a knowledge distillation framework specifically designed for semantic segmentation tasks in agricultural scenarios. This framework aims to address several prevalent challenges in smart agriculture, including limited computat...
Organizations face fundamental challenges in managing knowledge flows across complex networks, yet existing frameworks often lack quantitative tools for optimization. We develop a novel stochastic multi-scale model introducing knowledge flow viscosit...
Personalized recipe recommendation is attracting more and more attention, which can help people make choices from the exploding growth of online food information. Unlike other recommendation tasks, the target of recipe recommendation is a non-atomic...
To address the difficulty in detecting workers’ violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. Firs...
Automatic Modulation Recognition (AMR) is a critical technology for intelligent wireless communication systems, but the deployment of high-performance deep learning models is often hindered by their substantial computational and memory requirements....
Providing safely managed sanitation/hygiene requires key competencies for education, training, service delivery, enterprise development and management, product and infrastructure design and development, construction and installation, governance, fina...
Decision makers and policy analysts at different administrative levels often lack a holistic view of the problem as there are semantic variations in policy documents due to domain-specific content. For example, smart city initiatives are derived from...
Sustainability transitions in agriculture are explored through an analysis of niche initiatives within a common production system, relying on sustainable transitions, multi-level perspectives, and agroecological frameworks, and involving multi-actor,...
The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining...
The integrity and stability of railway fasteners are of vital importance to railway safety. To address the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions in rail fastener anomaly detection, this...
To tackle the complexity and limited applicability of high-precision segmentation models for damaged road markings, this study proposes a Multi-level Adaptive Lightweight Network (MALNet) based on knowledge distillation. By incorporating multi-scale...
8 April 2015
Objective: To explore the effect of a school-family-individual (SFI) multi-level education intervention model on knowledge and attitudes about accidental injuries among school-aged children to improve injury prevention strategies and reduce the incid...
With the significant expansion of civil aviation, particularly in the low-altitude economy, there is a significant gap between the escalating demand for airworthiness certification of novel aircraft designs, such as electric vertical take-off and lan...
Retinal blood vessel segmentation plays an important role for analysis of retinal diseases, such as diabetic retinopathy and glaucoma. However, retinal blood vessel segmentation remains a challenging task due to the low contrast between some vessels...
The success of large-scale deep learning models in remote sensing tasks has been transformative, enabling significant advances in image classification, object detection, and image–text retrieval. However, their computational and memory demands...
For good performance, most existing electrocardiogram (ECG) identification methods still need to adopt a denoising process to remove noise interference beforehand. This specific signal preprocessing technique requires great efforts for algorithm engi...
A convolutional neural network can easily fall into local minima for insufficient data, and the needed training is unstable. Many current methods are used to solve these problems by adding pedestrian attributes, pedestrian postures, and other auxilia...
The paper introduces a method for adaptive deductive synthesis of state models, of complex objects, with multilevel variable structures. The method makes it possible to predict the state of objects using the data coming from them. The data from the o...
Sepsis, a life-threatening condition causing significant global mortality, requires rapid diagnosis and intervention. Although recent advances in machine learning have supported clinical decision-making, existing sepsis classification approaches exhi...
The low-rank models have gained remarkable performance in the field of remote sensing image denoising. Nonetheless, the existing low-rank-based methods view residues as noise and simply discard them. This causes denoised results to lose many importan...
16 March 2012
A survey was conducted in Southern Malawi to examine the pattern of mothers’ knowledge on diarrhoea. Diarrhoea morbidity in the district is estimated at 24.4%, statistically higher than the national average at 17%. Using hierarchically built data fro...
The increasing availability of diverse internet of things devices has made possible an increase in zero-day attacks, but at the same time, it has brought up several issues related to data privacy, overhead communications, and computation scalability...
The current debate aims to reconceptualize the changing role and missions of the university in today’s knowledge economy and investigate how universities’ knowledge resources can benefit urban development and inform the direction of chang...
Hydro-climatic risk assessments at the regional scale are of little use in the risk treatment decision-making process when they are only based on local or scientific knowledge and when they deal with a single risk at a time. Local and scientific know...
Event argument extraction is a crucial subtask of event extraction, which aims at extracting arguments that correspond to argument roles when given event types. The majority of current document-level event argument extraction works focus on extractin...
The COVID-19 pandemic caused the world to pause temporarily on an almost planetary scale. The creation and diffusion of knowledge about environmental planning and public health are now almost taken for granted. However, such processes were rather dif...
In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer–employee, if–then, part–whole, product–producer, and cause–effect relations/causa...
Agroecology is recognized as a resilient agricultural system amid the ecological crisis, but also as a social movement working towards better livelihoods for farmers. In Burkina Faso, the dynamics among actors promoting agroecology are not well under...
Economics has become an essential component of secondary school curricula in many countries as a result of the growing awareness that young adults need fundamental economic knowledge to manage their personal finances. Accordingly, an increasing numbe...
Aspect-level sentiment analysis is a task of identifying and understanding the sentiment polarity of specific aspects of a sentence. In recent years, significant progress has been made in aspect-level sentiment analysis models based on graph convolut...
Plant disease symptoms combine local texture changes with patterns distributed across a leaf, while practical recognition models must remain compact. We introduce DC-FEN, a MobileNetV3-based design that models spatial-token relations and channel inte...
This paper explores the application of The World Avatar (TWA) dynamic knowledge graph to connect isolated data and assess the impact of rising sea levels in Singapore. Current sea level rise vulnerability assessment tools are often regional, narrow i...
In cloud manufacturing, Manufacturing Execution Systems (MES) must dynamically optimize MES-level process parameters under multi-objective and constraint-intensive conditions, including quality conformity, production-takt feasibility, energy consumpt...
10 April 2020
Based on the perspective of conservation of resources (COR) theory, this study adopts a multilevel approach to examine the influences of employees’ personal resources (i.e., work engagement and intrinsic motivation) and external resources (i.e....
The incompleteness of the existing knowledge graphs in the railway domain creates information gaps, impacting their quality and effectiveness in the operation and maintenance of high-speed railway turnout switch machines. To address this, we propose...
Sustainability competence is an important goal of Education for Sustainable Development (ESD) in school. It is therefore anchored in the education plans of almost all school tracks in Germany. However, empirical findings regarding ESD in schools are...
1 June 2026
Background: Birth defects remain a major global public health concern, particularly in low-resource settings where awareness and preventive practices are limited. Maternal knowledge, attitudes, and practices (KAP) are critical in the prevention and m...
Cancer is one of the biggest health burdens for women in the Middle East and North Africa (MENA), with the incidence of breast, cervical and colorectal cancer on the rise. Although preventive measures such as the HPV vaccination and population-based...
Women aged 18–49 years are women of childbearing age. Their nutrition knowledge (NK) is not only related to their physical health but also affects the diet quality of the entire family and the health status of the next generation. Studies that...
Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich...
Historic floors, including mosaics, stone slabs, and decorated pavements, are fragile elements that can be easily damaged during restoration works. Risks arise from falling tools, concentrated loads of scaffolding or equipment, and the repeated passa...
Due to the inadequacy in utilizing complementary information from different modalities and the biased estimation of degraded parameters, the unsupervised hyperspectral super-resolution algorithm suffers from low precision and limited applicability. T...
In times of climate change, periods of drought will occur more frequently. This causes challenges for water use, ranging from limitations on the navigability of water courses, limited availability of water for irrigation and drinking water supply, re...
Eco-labels are an instrument for enabling informed food choices and supporting a demand-sided change towards an urgently needed sustainable food system. Lately, novel eco-labels that depict a product’s environmental life cycle assessment on a m...
Most optimisation research focuses on relatively simple cases: one decision maker, one objective, and possibly a set of constraints. However, real-world optimisation problems often come with complications: they might be multi-objective, multi-agent,...
Advanced Persistent Threats (APTs) exhibit covert behaviors, long attack cycles, and fragmented intelligence, creating challenges for correlation analysis and attribution. This work proposes a unified knowledge-graph-based framework for multi-level A...
In this paper, the authors propose a multi-level model for the strategic competence of territorial units, which can help explain why some territorial units manage to respond appropriately to global challenges and thrive while others fail. Strategic c...
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