Variety Of Data Sources Research Articles

Enhancing management performance in rapidly changing organizations: a conceptual exploration

In today's rapidly evolving organizational landscape, effective management is crucial for survival and success. This study addresses the complex challenges managers face in dynamic environments, aiming to fill gaps in existing literature on strategies for improving management performance. Drawing on diverse secondary data sources, the research identifies key factors influencing management effectiveness, such as adaptive leadership, change management models, and the concept of learning organizations. The review highlights that successful management in rapidly changing organizations requires strategic foresight, adaptability, and a culture of innovation, amidst challenges posed by technological advancements, globalization, and workforce diversity. The findings emphasize the critical role of communication, talent management, and organizational culture in shaping effective management practices. This study enhances understanding of management in dynamic contexts, offering practical implications for both industry and academia. Future research should focus on industry-specific applications, theoretical exploration, and methodological enhancements to further advance management practices. Despite limitations associated with secondary data, this study provides valuable insights into strategies that enhance management performance, ultimately enhancing organizational resilience and competitiveness in a constantly changing environment

Mapping the future of antimicrobial use data collection in US animal agriculture: insights from FDA-funded pilot studies.

Antimicrobial use (AMU) contributes to the emergence of antimicrobial resistance, necessitating antimicrobial stewardship actions across all sectors using a One Health approach to preserve antimicrobial effectiveness. This overview delves into 2 FDA-funded projects focused on collecting and analyzing AMU data in major food-producing animal species (cattle, swine, turkeys, and chickens). Initiated in 2016, the projects aimed to establish baseline AMU information and pilot methodologies. This article describes the methodologies used by grantees for data collected from 2016 to 2021, emphasizing the diverse data sources and metrics utilized. Instead of summarizing the trends, it provides a list of publications generated from the grants. Factors contributing to successful data collection included early interaction and trust building between the producers/data holders and researchers. Shared challenges include limitations stemming from convenience sampling, variable industry participation, and lack of data covering all segments of a particular commodity (eg, data on breeding or young animals were lacking). Future collaborative efforts are needed to enhance data standardization, contextualization, representativeness, and reporting of national-scale AMU data going forward. Addressing these challenges and data gaps is essential for effective monitoring of AMU in veterinary settings and animal agriculture, in alignment with national strategies to combat antimicrobial resistance.

Multimodal data fusion enhanced deep learning prediction of crack path segmentation in CFRP composites

Carbon fiber-reinforced polymer (CFRP) composites are extensively used in various engineering applications due to their superior strength-to-weight ratio and excellent mechanical properties. Predicting crack propagation paths in CFRP composites is a complex challenge due to their multiphase nature and intricate microstructural interactions. While finite element (FE) simulations possess significant capabilities for this purpose, they entail substantial computational demands and extended execution times, thereby limiting their viability in applications with high computational requirements. To address this challenge, we propose an end-to-end deep learning framework specifically for predicting crack propagation paths in two-dimensional CFRP composites. Drawing inspiration from semantic segmentation techniques, we employ EfficientNet for feature extraction, enabling the capture of hierarchical and multiscale features from both microstructure images and stress field distributions. A key aspect of our framework is the utilization of multimodal data fusion and self-attention mechanisms to effectively integrate these diverse data sources. The results demonstrate the effectiveness of our multimodal feature integration approach, producing accurate segmentations of crack path. This novel framework offers a promising approach to understanding and predicting failure mechanisms in composite materials, with significant implications for the design and maintenance of advanced composite structures.

Fostering urban resilience and accessibility in cities: A dynamic knowledge graph approach

This paper explores the utilisation of knowledge graphs and an agent-based implementation to enhance urban resilience and accessibility in city planning. We expand The World Avatar (TWA) dynamic knowledge graph to support decision-making in disaster response and urban planning. By employing a set of connected agents and integrating diverse data sources — including flood data, geospatial building information, land plots, and open-source data — through sets of ontologies, we demonstrate disaster response in a coastal town in the UK and various aspects relevant to city planning for a mid-sized town in Germany using TWA. In King’s Lynn, our agent-based approach facilitates holistic disaster response by calculating optimal routes, avoiding flooded segments dynamically, assessing infrastructure accessibility before and during a flood using isochrones, identifying inaccessible population areas, guiding infrastructure restoration, and conducting critical path analysis. In Pirmasens, for city planning purposes, the knowledge graph-driven isochrone generation provides evidence-based insights into current amenity coverage and enables scenario planning for future amenities while adhering to land regulations. The implementation of agents and knowledge graphs achieves interoperability and enhances urban resilience and accessibility by enabling cross-domain correlation analysis that extends various areas including geospatial buildings, population demographics, accessibility coverage, and land use regulations.

Big data for epidemic preparedness in southeast Asia: An integrative study

This integrative study explores the pivotal role of big data in enhancing epidemic preparedness and response strategies in Southeast Asia. The region, characterized by its diverse socio-economic landscape and frequent exposure to infectious disease threats, necessitates innovative approaches to mitigate the impact of epidemics. Big data, with its vast and varied sources, offers unprecedented opportunities to revolutionize epidemic surveillance, prediction, and management. Drawing on a comprehensive review of literature and case studies, this paper delves into the multifaceted applications of big data in epidemic preparedness. It examines the utilization of diverse data sources, including social media, mobile phones, and IoT devices, for real-time monitoring of disease outbreaks and trends. Moreover, it elucidates the role of advanced analytics techniques, such as machine learning and data visualization, in informing timely and targeted response strategies. Furthermore, this study underscores the importance of collaborative efforts and partnerships in leveraging big data for epidemic preparedness. It explores successful initiatives that have demonstrated the effectiveness of data sharing and interdisciplinary collaboration in enhancing epidemic response capabilities across borders. However, the integration of big data into epidemic preparedness initiatives presents various challenges, including data quality issues, privacy concerns, and the need for robust infrastructure and expertise. Addressing these challenges requires concerted efforts from governments, healthcare organizations, and the private sector. In conclusion, this study advocates for continued research, investment, and policy support to harness the full potential of big data in epidemic preparedness. By embracing innovative technologies and fostering collaboration, Southeast Asia can build resilient health systems capable of effectively combating emerging infectious disease threats.

Harnessing Data Analytics for Enhanced Understanding and Management of Depression Disorders in Mental Health Care

Depression is a widespread and incapacitating mental health condition that affects millions of people around the world.The integration of data analytics into mental health care presents a transformative opportunity to enhance the understanding, diagnosis, and management of depression. This paper explores the application of data analytics in identifying patterns and trends from diverse data sources such as electronic health records (EHRs) and social media. Through advanced techniques including machine learning, natural language processing (NLP), and predictive modeling, data analytics facilitates early detection of depressive symptoms, the development of personalized treatment plans, and continuous patient monitoring. Additionally, the aggregation and analysis of large- scale data provide valuable insights for public health strategies aimed at reducing the prevalence and impact of depression. This paper highlights the critical role of interdisciplinary collaboration in leveraging data analytics to improve mental health outcomes and underscores the importance of robust safeguards to ensure patient confidentiality and trust. Keywords Data Analytics, Depression, Mental Health, Predictive Modeling, Machine Learning, Natural Language Processing, Electronic Health Records

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.

Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA-disease associations (MDAs), miRNA-target interactions (MTIs), and disease-gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.

A Nursing Clinical Care Services Platform to Support Leadership Decision Making.

Like most organizations, The Children's Hospital of Philadelphia (CHOP) generates a lot of data of varying types, from clinical to staffing and operations. However, these datasets are separated, preventing effective analytics. To improve workforce management and care delivery, CHOP's Nursing department collaborated with the Data & Analytics team to create a dashboard-based platform for analyzing workforce metrics and patient outcomes, aiding nursing leaders in decision-making. This project involved multiple phases with contributions from Nursing, Data & Analytics, CHOP's Data Trust Office, and Human Resources. Key performance indicators were identified, and a variety of data sources were aggregated to provide a comprehensive view of the nursing enterprise. The resulting platform offers automated, current, and reliable analytics on various aspects, including nursing demographics, education, survey results, staffing actuals by job group, and patient and family experience data. The platform's usability was assessed using a modified Health Information Technology Usability Evaluation Scale survey, with a 29% response rate primarily from senior directors and managers. The findings showed high usability and satisfaction, indicating the dashboard is a valuable decision-support tool. Lessons learned include the need for analytics education for nurses and mid-managers, the inclusion of critical nursing-specific metrics (and development of the data pipelines making them possible), and the integration of multidisciplinary team metrics for comprehensive nursing analytics.

A data-driven multi-perspective approach to cybersecurity knowledge discovery through topic modelling

Cybersecurity is crucial for protecting the privacy of digital systems, maintaining economic stability, and ensuring national security. This study presents a comprehensive approach to cybersecurity knowledge discovery through topic modelling, using a multi-perspective analysis of academic and industry sources. The datasets include 15,751 articles from the Web of Science (WoS) database and 5,831 articles from Security Magazine, spanning from 2011 to 2023. We employed BERTopic for topic modelling, UMAP for dimensionality reduction, and HDBSCAN clustering algorithm for grouping and analysing distinct article clusters to uncover latent topics, enhancing the understanding of the evolving cybersecurity landscape. This study found 12 micro-clusters and three macro-clusters, namely technology, smart city and education, from the WoS database and 12 more micro-clusters and four macro-clusters, including organization, public security, governance, and education, from magazines. This study reveals key cybersecurity research and practice trends, such as the increasing focus on malware, ransomware, and cyber-attack mitigation. Additionally, temporal analysis indicates a significant rise in cybersecurity interest around 2020, followed by a diversification of topics. The results highlight the importance of integrating diverse data sources to capture a holistic view of cybersecurity developments. Future work will aim to refine the clustering algorithms to further improve topic extraction and analysis and expand the datasets to include more diverse sources and perspectives. This approach helps discover current cybersecurity trends and provides a foundation for more targeted and effective cybersecurity strategies.

The Chilean exposome-based system for ecosystems (CHiESS): a framework for national data integration and analytics platform.

The double burden of diseases and scarce resources in developing countries highlight the need to change the conceptualization of health problems and translational research. Contrary to the traditional paradigm focused on genetics, the exposome paradigm proposed in 2005 that complements the genome is an innovative theory. It involves a holistic approach to understanding the complexity of the interactions between the human being’s environment throughout their life and health. This paper outlines a scalable framework for exposome research, integrating diverse data sources for comprehensive public health surveillance and policy support. The Chilean exposome-based system for ecosystems (CHiESS) project proposes a conceptual model based on the ecological and One Health approaches, and the development of a technological dynamic platform for exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. CHiESS considers a multilevel exposure for exposome operationalization, including the ecosystem, community, population, and individual levels. CHiESS will include four consecutive stages for development into an informatic platform: (1) environmental data integration and harmonization system, (2) clinical and omics data integration, (3) advanced analytical algorithm development, and (4) visualization interface development and targeted population-based cohort recruitment. The CHiESS platform aims to integrate and harmonize available secondary administrative data and provide a complete geospatial mapping of the external exposome. Additionally, it aims to analyze complex interactions between environmental stressors of the ecosystem and molecular processes of the human being and their effect on human health. Moreover, by identifying exposome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation. Utilizing advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, this framework enhances data security, real-time monitoring, and predictive analytics. The CHiESS model is adaptable for international use, promoting global health collaboration and supporting sustainable development goals.

Addressing the gaps: What social media tells us about patient experiences with hypoglossal nerve stimulation

PurposeHypoglossal nerve stimulation (HNS) can be an effective treatment for moderate to severe obstructive sleep apnea (OSA) in positive airway pressure (PAP) intolerant patients. To better understand patient perceptions of HNS therapy, we explored three Facebook groups pertaining to HNS therapy. Materials and methodsA retrospective analysis of Facebook posts from three HNS-related Facebook groups, from October 1, 2022 to October 1, 2023, was performed. Posts were analyzed for author attitude, content (adverse events, inquiries, or sharing information), and the inclusion of media within the post. ResultsFrom 737 Facebook posts, 752 events were identified, predominantly authored by patients or family (99.5 %). Few posts included media attachments (7.3 %), primarily photos (85.5 %). Post tone was mainly neutral (79.4 %), rather than positive (12.9 %) or negative (7.7 %). Most posts (53.6 %) were queries to the Facebook group, as opposed to sharing information (28.9 %) or detailing adverse events (17.6 %). Notably queries posed by Facebook group members included those pertaining to the postoperative recovery course (10.17 %), physical activity restrictions (6.20 %), HNS therapy eligibility (4.71 %), financial considerations (4.22 %), and more. ConclusionOverall, Facebook group members reported a predominantly neutral tone, typically posting queries to the group rather than sharing information or detailing adverse events. These findings illustrate how diverse data sources, such as social media, can enhance our understanding of patient experiences and identify gaps in patient education with HNS therapy.

Understanding the Biomechanics of Musculoskeletal Injuries in High-Risk Environmental Conditions

The present study examines the biomechanics of musculoskeletal injuries in high-risk environmental conditions by thoroughly analyzing diverse data sources and employing various methodologies. This study utilizes motion analysis, force sensor measurements, computer simulations, and biomechanical testing to examine the various factors contributing to musculoskeletal injuries. The analysis of motion indicates that specific tests, namely varus test, impose considerably greater biomechanical stress, thereby emphasizing their susceptibility to causing injuries. The force sensor data reveals that pressure mapping system is responsible for exerting the highest force, raising concerns regarding its potential impact on the risk of injury. According to computer simulations, various injury risks are associated with different conditions, with load carriage exhibiting the highest risk. The analysis of joint stability during biomechanical testing reveals discrepancies in joint stability levels across different tasks. Outliers within the dataset highlight tasks that exhibit notable concerns regarding joint stability. Moreover, supplementary motion analysis data about various task variants, such as Sulcus sign and vasus stress test unveils distinct variations that lead to heightened levels of biomechanical stress. The discoveries mentioned above offer valuable perspectives on the biomechanical foundations of musculoskeletal injuries in environments with elevated risk levels. The aforementioned findings emphasize the necessity of implementing focused interventions, enhancing equipment design, and implementing heightened safety measures to reduce the risks of injury effectively. The present study establishes a fundamental basis for subsequent research endeavors and proposes approaches designed to safeguard the welfare of individuals operating in demanding contexts.

Development and implementation of a core genome multilocus sequence typing scheme for Yersinia enterocolitica: a tool for surveillance and outbreak detection.

Yersinia enterocolitica (Y. enterocolitica) is the most frequent etiological agent of yersiniosis and has been responsible for several national outbreaks in Norway and elsewhere. A standardized high-resolution method, such as core genome Multilocus Sequence Typing (cgMLST), is needed for pathogen traceability at the national and international levels. In this study, we developed and implemented a cgMLST scheme for Y. enterocolitica. We designed a cgMLST scheme in SeqSphere + using high-quality genomes from different Y. enterocolitica biotype sublineages. The scheme was validated if more than 95% of targets were found across all tested Y. enterocolitica: 563 Norwegian genomes collected between 2012 and 2022 and 327 genomes from public data sets. We applied the scheme to known outbreaks to establish a threshold for identifying major complex types (CTs) based on the number of allelic differences. The final cgMLST scheme included 2,582 genes with a median of 97.9% (interquartile range 97.6%-98.8%) targets found across all tested genomes. Analysis of outbreaks identified all outbreak strains using single linkage clustering at four allelic differences. This threshold identified 311 unique CTs in Norway, of which CT18, CT12, and CT5 were identified as the most frequently associated with outbreaks. The cgMLST scheme showed a very good performance in typing Y. enterocolitica using diverse data sources and was able to identify outbreak clusters. We recommend the implementation of this scheme nationally and internationally to facilitate Y. enterocolitica surveillance and improve outbreak response in national and cross-border outbreaks.

Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions

The study of the environmental transition of the aviation sector calls for prospective traffic scenarios. Detailed traffic and emissions inventories are often needed to refine the available analyses and to enable the simulation of regionalised scenarios. In the past studies, these are generally based on commercial, proprietary traffic data, making their dissemination problematic and reducing the reproducibility of the science produced. Open-source alternatives do exist but with limited geographical coverage. This study bridges this gap by presenting an innovative open-source dataset detailing 2019's global air traffic flows and associated CO2 emissions. A comprehensive approach that compiles diverse flight data sources is presented. The remaining data gaps are addressed by constructing a route network through systematic Wikipedia parsing and by estimating the related traffic using socio-economic data. Then, an aircraft performance model to estimate CO2 emissions is implemented. This methodology promises reinforced reproducibility and broader data accessibility in aviation environmental research. Several reference datasets are used to evaluate the accuracy of the open-source dataset. Despite various levels of accuracy for individual routes, major traffic flows are well estimated at the country and continental levels, albeit with room for refinement to ensure consistent data reliability. To facilitate the exploration of the dataset, the AeroSCOPE tool has been developed. To initiate research prospects, use cases of this dataset are proposed, concerning the network potential of electric and hydrogen-powered aircraft and inequalities in air transport.

A Comprehensive Framework for Pathology Classification Bridging Precision and Interpretability

Pathology classification is an indispensable component of medical diagnostics, facilitating accurate disease identification, prognosis determination, and treatment planning. However, the increasing complexity and heterogeneity of pathological manifestations pose significant challenges to traditional classification methodologies. This abstract presents a novel framework that integrates advanced machine learning techniques with domain-specific expertise to enhance the precision and interpretability of pathology classification. Our framework adopts a multi-modal approach, leveraging diverse data sources including histopathological images, clinical records, genomic profiles, and molecular biomarkers. Through feature fusion and dimensionality reduction techniques, we effectively capture intricate patterns and latent relationships embedded within the data, enabling robust classification across diverse pathological conditions. Furthermore, interpretability is prioritized through the incorporation of explainable AI methodologies, facilitating the identification of salient features and decision rationales underlying classification outcomes. This ensures transparency and trustworthiness in the diagnostic process, empowering clinicians to make informed decisions and refine treatment strategies. Validation of our framework across various pathological contexts demonstrates superior performance compared to conventional approaches, exhibiting high accuracy, sensitivity, and specificity. Moreover, its modular architecture facilitates customization and scalability, accommodating evolving diagnostic needs and emerging technological advancements. In conclusion, our proposed framework represents a significant advancement in pathology classification, offering a synergistic blend of computational sophistication and clinical relevance. By seamlessly integrating cutting-edge technologies with domain knowledge, it holds promise for revolutionizing diagnostic practices and improving patient outcomes in the realm of precision medicine.

Mechanisms for Data Acquisition to Train Artificial Intelligence Models for Detecting Increased Susceptibility to Fire Situations by Using Internet of Things Devices and Satellite Systems

Aim: Exploration and developing mechanisms of advanced data acquisition necessary for training an artificial intelligence model capable of effectively detecting areas with increased susceptibility to fire situations. The study focuses on utilizing data from satellite missions and ground-based sensors, which provide both high-resolution imagery and precise data on temperature, humidity, and other environmental factors. By analysing these diverse data sources, the research aims to create a comprehensive and efficient model capable of early detection of potential fire hazards, which is crucial for prevention for fire-prone situations. Project and methods: It centres on a project that aims to enhance fire detection and management through the integration of artificial intelligence with data acquired from satellite systems and internet of things devices. The methodologies employed in this project involve a combination of advanced data acquisition, machine learning techniques, and the synthesis of diverse environmental data to train artificial intelligence models that can predict and detect fire incidents more effectively. Results: Significant advancements in fire detection and management have been demonstrated through the integration of artificial intelligence (AI) with satellite data and IoT: 1. Enhanced monitoring capabilities the use of satellite data systems enabled real-time monitoring of thermal anomalies and vegetation health, crucial for early detection and effective monitoring of wildfires. This real-time capability allowed for quicker responses and more informed decision-making in firefighting efforts. 2. Effective integration of data sources: the integration of satellite and surface data proved to be effective in enhancing the predictive capabilities of the fire management systems. This comprehensive approach allowed for a better understanding of fire dynamics and contributed to more accurate and timely predictions. Conclusions: It could be emphasize the significant benefits and future potential of integrating artificial intelligence with satellite and internet of things data for improving fire detection and management. The integration of satellite imagery and internet of things sensor data is essential for enhancing the predictive accuracy of artificial intelligence systems. This integration allows for a comprehensive assessment of fire risks, providing actionable intelligence that is critical for prevention for fire-prone situations. These conclusions underscore the transformative potential of artificial intelligence in enhancing fire management systems. Keywords: data acquisition, artificial intelligence, IoT, satellite data systems, fire management systems

Artificial intelligence literacy: a proposed faceted taxonomy

PurposeThe purpose of this paper is to propose a taxonomy of artificial intelligence (AI) literacy to support AI literacy education and research.Design/methodology/approachThis study makes use of the facet analysis technique and draws upon various sources of data and information to develop a taxonomy of AI literacy. The research consists of the following key steps: a comprehensive review of the literature published on AI literacy research, an examination of well-known AI classification schemes and taxonomies, a review of prior research on data/information/digital literacy research and a qualitative and quantitative analysis of 1,031 metadata records on AI literacy publications. The KH Coder 3 software application was used to analyse metadata records from the Scopus multidisciplinary database.FindingsA new taxonomy of AI literacy is proposed with 13 high-level facets and a list of specific subjects for each facet.Research limitations/implicationsThe proposed taxonomy may serve as a conceptual AI literacy framework to support the critical understanding, use, application and examination of AI-enhanced tools and technologies in various educational and organizational contexts.Practical implicationsThe proposed taxonomy provides a knowledge organization and knowledge mapping structure to support curriculum development and the organization of digital information.Social implicationsThe proposed taxonomy provides a cross-disciplinary perspective of AI literacy. It can be used, adapted, modified or enhanced to accommodate education and learning opportunities and curricula in different domains, disciplines and subject areas.Originality/valueThe proposed AI literacy taxonomy offers a new and original conceptual framework that builds on a variety of different sources of data and integrates literature from various disciplines, including computing, information science, education and literacy research.

Artificial intelligence-enhanced patient evaluation: bridging art and science.

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

The Future of Neurodiagnosis: Deep Learning for Earlier Intervention

This study presents an innovative deep learning framework for improved early detection of a debilitating neurodegenerative condition marked by cognitive decline and memory impairment. Timely diagnosis is crucial for effective interventions and improved patient outcomes. Our framework integrates diverse data sources, including structural and functional neuroimaging (MRI and PET) alongside clinical information, to enhance detection precision. Convolutional Neural Networks (CNNs) analyze structural MRI scans, extracting subtle changes in brain structure indicative of early disease progression. Functional insights are gleaned from PET scans, contributing to increased sensitivity. Additionally, longitudinal data is incorporated through Recurrent Neural Networks (RNNs) to capture the disease's temporal evolution. Training on a diverse dataset utilizes transfer learning, optimizing performance even with limited labeled data. Rigorous validation consistently demonstrates the model's effectiveness, achieving a 92% accuracy rate.

A knowledge graph-aided decision guidance method for product conceptual design

In the fast-paced world of product design, businesses seek a competitive edge by swiftly addressing user requirements and developing precise solutions. Depending on diverse knowledge, conceptual design makes enterprises invest significantly in knowledge management. Following the philosophy of decision-based design, a knowledge graph-aided decision guidance method is proposed to streamline and enhance knowledge utilisation in product conceptual design. Firstly, the decision-making process in product conceptual design is modelled using the Concept-Decision-Knowledge (CDK) model, yielding the meta-model of CDK (mCDK). A data-augmented BERT-BiLSTM-CRF model is adopted to extract information from diverse data sources, forming a CDK knowledge graph (CDK-KG). A decision case for employing problem-solving knowledge configuration is generated to resolve specific design decision problems when new requirements arise. Validation of the method is demonstrated through a case study on the launch vehicle conceptual design of the first and second-stage separation system. The results indicate that the proposed method can automatically extract information from data resources to construct a knowledge graph. Furthermore, it can provide decision cases for design requirements through knowledge configuration, supporting decision-making.