Integration Of Information Research Articles

Knowledge graph for integration and quality traceability of agricultural product information

Knowledge graph for integration and quality traceability of agricultural product information

A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies.

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes. A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent. In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level. The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Quantum and coherent signal transmission on a single-frequency channel via the electro-optic serrodyne technique.

Fiber-optical networks are well established to accommodate global data traffic via coherent information transmission. The next generation of telecommunications will require the integration of quantum information into fiber-optic networks, e.g., for quantum key distribution. A promising and scalable route to enable quantum networking is encoding quantum information into the frequency of photons. While the cointegration of frequency-entangled photons with coherent information transmission is achieved via spectral multiplexing, more resource-efficient approaches are required. In this work, we introduce and experimentally demonstrate a transceiver concept that enables the transmission of coherent and frequency-entangled photons over a single-frequency channel. Our concept leverages the serrodyne technique via electro-optic phase modulation leading to very different dynamics for entangled and coherent photons. This enables temporal multiplexing of the respective signals. We demonstrate the preservation of entanglement over the channel in the presence of coherent light. Our approach reveals a strong potential for efficient bandwidth use in hybrid networks.

Advances in Deep Learning Methods for Prostate Segmentation and Volume Estimation in Ultrasound Imaging

Accurate prostate volume estimation is crucial for effective prostate disease management. Ultrasound (US) imaging, particularly transrectal ultrasound, offers a cost-effective and rapid assessment. However, US images often suffer from artifacts and poor contrast, making prostate volume estimation challenging. This review explores recent advancements in deep learning (DL) techniques for automatic prostate segmentation in US images as a primary step toward prostate volume estimation. We examine various DL architectures, including traditional U-Net modifications and innovative designs incorporating residual connections, multi-directional image data, and attention mechanisms. Additionally, we discuss pre-processing methods to enhance image quality, the integration of shape information, and strategies to improve the consistency and robustness of DL models. The effectiveness of these techniques is evaluated through metrics such as the Dice Similarity Coefficient, Jaccard Index, and Hausdorff Distance. The review highlights the potential of DL in improving prostate volume estimation accuracy and reducing clinical workload while also identifying areas for future research to enhance model performance and generalizability.

Improved diabetic retinopathy severity classification using squeeze-and-excitation and sparse light weight multi-level attention u-net with transfer learning from xception.

Diabetic Retinopathy (DR) is a significant cause of vision loss in diabetic patients, making early detection and accurate severity classification essential for effective management and prevention. This study aims to develop an enhanced DR severity classification approach using advanced model architectures and transfer learning to improve diagnostic accuracy and support better patient care. We propose a novel model, Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet), designed to classify DR severity using fundus images from the Messidor 1 and Messidor 2 datasets. The XceSE_SparseLwMLA-UNet integrates several advanced mechanisms: the Squeeze-and-Excitation (SE) mechanism for adaptive feature recalibration, the Sparse Lightweight Multi-Level Attention (SparseLwMLA) mechanism for effective contextual information integration, and transfer learning from the Xception architecture to enhance feature extraction capabilities. The SE mechanism refines channel-wise feature responses, while SparseLwMLA enhances the model's ability to identify complex DR patterns. Transfer learning utilizes pre-trained weights from Xception to improve generalization across DR severity levels. The proposed XceSE_SparseLwMLA-UNet model demonstrates superior performance in DR severity classification, achieving higher accuracy and improved multi-class F1 scores compared to existing models. The model's color-coded segmentation outputs offer interpretable visual representations, aiding medical professionals in assessing DR severity levels. The XceSE_SparseLwMLA-UNet model shows promise for advancing early DR diagnosis and management by enhancing classification accuracy and providing valuable visual insights. Its integration of advanced architectural features and transfer learning contributes to better patient care and improved visual health outcomes.

Application of Genomic Technology in Early Diagnosis and Personalized Treatment for Cancer Patients

This journal article delves into the transformative impact of genomic technology in the realm of cancer diagnosis and treatment, specifically focusing on early detection and personalized therapeutic interventions. Genomic advancements have revolutionized our understanding of cancer at the molecular level, offering unprecedented insights into the genetic basis of the disease. This study explores the application of genomic technology to enable early diagnosis by identifying specific genetic markers associated with different cancer types. The research also investigates the integration of genomic information into personalized treatment plans, tailoring interventions based on the unique genetic profile of individual patients. Precision medicine, guided by genomic data, has shown promising results in improving treatment outcomes and minimizing adverse effects. The article reviews key genomic technologies, such as next-generation sequencing and gene expression profiling, highlighting their role in elucidating the intricate genetic landscape of cancer. Furthermore, ethical considerations and challenges associated with the implementation of genomic technology in cancer care are discussed. Issues about patient privacy, data security, and the need for informed consent are addressed to ensure responsible and patient-centered genomic practices. The findings underscore the potential of genomic technology to revolutionize cancer care, ushering in an era of early detection and personalized treatment strategies. As we move towards an era of precision oncology, understanding the implications and nuances of genomic applications becomes crucial for healthcare practitioners, researchers, and policymakers alike.

Incomplete Multiview Clustering Based on Consensus Information.

In contrast to traditional single-view clustering methods, multiview clustering (MVC) approaches aim to extract, analyze, and integrate structural information from diverse perspectives, providing a more comprehensive understanding of internal data structures. However, with an increasing number of views, maintaining the integrity of view information becomes challenging, giving rise to incomplete MVC (IMVC) methods. While existing IMVC methods have shown notable performance on many incomplete multiview (IMV) databases, they still grapple with two key shortcomings: 1) they treat the information of each view as a whole, disregarding the differences among samples within each view; and 2) they rely on eigenvalue and eigenvector operations on the view matrix, limiting their scalability for large-scale samples and views. To overcome these limitations, we propose a novel multiview clustering with consistent information (IMVC-CI) of sample points. Our method explores the multiview information set of sample points to extract consensus structural information and subsequently restores unknown information in each view. Importantly, our approach operates independently on individual sample points, eliminating the need for eigenvalue and eigenvector operations on the view information matrix and facilitating parallel computation. This significantly enhances algorithmic efficiency and mitigates challenges associated with dimensionality. Experimental results on various public datasets demonstrate that our algorithm outperforms state-of-the-art IMVC methods in terms of clustering performance and computational efficiency. The code for our article has been uploaded to https://github.com/PhdJiayiTang/IMVC-CI.git.

Enhanced Post-Movement Beta Rebound: Unraveling the Impact of Preplanned Sequential Actions

The Post-Movement Beta Rebound (PMBR) is the increase in beta-band power after voluntary movement ends, but its specific role in cognitive processing is unclear. Current theory links PMBR with updates to internal models, mental frameworks that help anticipate and react to sensory feedback. However, research has not explored how reactivating a preexisting action plan, another source for internal model updates, might affect PMBR intensity. To address this gap, we recruited 20 participants (mean age 18.55 ± 0.51; 12 females) for an experiment involving isolated (single-step) or sequential (two-step) motor tasks based on predetermined cues. We compared PMBR after single-step movements with PMBR after the first movement in two-step tasks to assess the influence of a subsequent action on the PMBR power associated with the first action. The results show a significant increase in PMBR magnitude after the first movement in sequential tasks compared to the second action and the isolated movements. Notably, this increase is more pronounced for right-hand movements, suggesting lateralized brain activity in the left hemisphere. These findings indicate that PMBR is influenced not only by external stimuli but also by internal cognitive processes such as working memory. This insight enhances our understanding of PMBR's role in motor control, emphasizing the integration of both external and internal information.

Three-Dimensional Deformation Estimation from Multi-Temporal Real-Scene Models for Landslide Monitoring

This study proposes a three-dimensional (3D) deformation estimation framework based on the integration of shape and texture information for real-scene 3D model matching, effectively addressing the issue of deformation assessment in large-scale geological landslide areas. By extracting and merging the texture and shape features of matched points, correspondences between points in multi-temporal real-scene 3D models are established, resolving the difficulties faced by existing methods in achieving robust and high-precision 3D point matching over landslide areas. To ensure the complete coverage of the geological disaster area while enhancing computational efficiency during deformation estimation, a voxel-based thinning method to generate interest points is proposed. The effectiveness of the proposed method is validated through tests on a dataset from the Lijie north hill geological landslide area in Gansu Province, China. Experimental results demonstrate that the proposed method significantly outperforms existing classic and advanced methods in terms of matching accuracy metrics, and the accuracy of our deformation estimates is close to the actual measurements obtained from GNSS stations, with an average error of only 2.2 cm.

Digital Literacy In Detecting Hoaxes In Instagram Media (Analysis Study Of Communication Science Students, State Islamic University Of North Sumatra)

Abstract This article explores the urgency of digital literacy in detecting hoaxes on social media, especially Instagram, among students of Communication Sciences of the State Islamic University of North Sumatra (UINSU). This research identifies crucial problems in the spread of false information or hoaxes that have the potential to divide society and damage information integrity. This study uses a qualitative methodology, this research adopts an ethnographic approach and a constructive paradigm to gain a deep understanding of how students perceive and respond to the information they find on social media. The location of this study is Campus 4 Tuntungan UINSU, and the resource persons were selected through purposive sampling based on their experience and activeness in using Instagram as well as their involvement in digital literacy issues. Primary data sources are obtained from in-depth interviews and participatory observations, while secondary data sources include relevant document studies. The data analysis technique used is the Miles, Huberman, and Saldana analytical model which includes data reduction, data presentation, and conclusion drawn. The validity test of the data was carried out through source triangulation. The results of the study show that even though students have access to digital literacy tools, there is still a tendency to trust and spread unverified content. The conclusion of the study emphasizes that digital literacy is not only important as a tool to verify information and avoid the spread of hoaxes, but also as a means to increase critical awareness of media content.

The Mediating Role of Teacher Training and Learner Orientation in ICT Integration for Competence-Based Curriculum Implementation in Kyaka II Refugee Settlement

This study explored the impact of teacher training and learner orientation on the integration of Information and Communication Technologies (ICTs) within the Competence-Based Curriculum (CBC) in secondary schools in Kyaka II Refugee Settlement. Despite significant efforts to improve education in refugee contexts, effective ICT integration remains a complex challenge. The research examined how teacher training and learner orientation influence ICT usage, teacher preparedness, and student engagement in this distinctive educational environment. Utilizing a descriptive survey design and questionnaires, data was collected from 455 participants, including 89 purposively selected teachers and 366 randomly selected students. Data was analyzed using SPSS to generate descriptive statistics. The findings indicate that comprehensive teacher training positively affects ICT integration, with trained educators demonstrating enhanced confidence and competence in using ICT tools, as evidenced by a moderate positive correlation coefficient (r = 0.54). Oriented learners exhibited increased interest and engagement in ICT-driven learning. The study identified a gap between training outcomes and practical classroom application, suggesting a need for more hands-on and continuous professional development. Challenges such as inadequate ICT infrastructure, poor internet connectivity, and limited administrative support were significant barriers to optimal ICT integration. The study recommends implementing ongoing, hands-on teacher training programs, improving ICT infrastructure, establishing robust support systems within schools, and enhancing student access to ICT resources to address these challenges and support effective ICT integration in refugee education settings

Research on the Application of Geographic Information Systems in the Construction of Smart Cities

With the continuous deepening of globalization and informatization, the construction of smart cities is gradually becoming a core strategy in the process of urban modernization. In this strategy, Geographic Information Systems (GIS) play a crucial technical support role. This article adopts a case study method, taking the construction of a smart city in Nanjing as the specific research object, and deeply explores the practical application of GIS in key fields such as urban planning, environmental protection, and traffic management. Through comprehensive analysis of these examples, this article systematically summarizes the significant achievements of GIS technology in improving urban management efficiency, optimizing decision support systems, enhancing public service levels, and promoting sustainable urban development. The research results indicate that GIS technology provides solid data support and decision-making support for the core areas of cities through precise integration and analysis of geographic information. GIS technology not only optimizes the layout of urban infrastructure, but also significantly enhances the city's response to emergency events through real-time monitoring and dynamic simulation, while promoting efficient allocation and utilization of urban resources. The research conclusion points out that, GIS technology is the cornerstone of smart city construction, greatly enhancing the scientific and forward-looking nature of urban management. The effective application of GIS has opened up new avenues for intelligent and refined urban management, significantly improving the quality of life of residents and providing solid technical support for the sustainable development of cities.

Evolving brain network dynamics in early childhood: Insights from modular graph metrics

Modular dynamic graph theory metrics effectively capture the patterns of dynamic information interaction during human brain development. While existing research has employed modular algorithms to examine the overall impact of dynamic changes in community structure throughout development, there is a notable gap in understanding the cross-community dynamic changes within different functional networks during early childhood and their potential contributions to the efficiency of brain information transmission. This study seeks to address this gap by tracing the trajectories of cross-community structural changes within early childhood functional networks and modeling their contributions to information transmission efficiency. We analyzed 194 functional imaging scans from 83 children aged 2 to 8 years, who participated in passive viewing functional magnetic resonance imaging sessions. Utilizing sliding windows and modular algorithms, we evaluated three spatiotemporal metrics—temporal flexibility, spatiotemporal diversity, and within-community spatiotemporal diversity—and four centrality metrics: within-community degree centrality, eigenvector centrality, between-community degree centrality, and between-community eigenvector centrality. Mixed-effects linear models revealed significant age-related increases in the temporal flexibility of the default mode network (DMN), executive control network (ECN), and salience network (SN), indicating frequent adjustments in community structure within these networks during early childhood. Additionally, the spatiotemporal diversity of the SN also displayed significant age-related increases, highlighting its broad pattern of cross-community dynamic interactions. Conversely, within-community spatiotemporal diversity in the language network exhibited significant age-related decreases, reflecting the network's gradual functional specialization. Furthermore, our findings indicated significant age-related increases in between-community degree centrality across the DMN, ECN, SN, language network, and dorsal attention network, while between-community eigenvector centrality also increased significantly for the DMN, ECN, and SN. However, within-community eigenvector centrality remained stable across all functional networks during early childhood. These results suggest that while centrality of cross-community interactions in early childhood functional networks increases, centrality within communities remains stable. Finally, mediation analysis was conducted to explore the relationships between age, brain dynamic graph metrics, and both global and local efficiency based on community structure. The results indicated that the dynamic graph metrics of the SN primarily mediated the relationship between age and the decrease in global efficiency, while those of the DMN, language network, ECN, dorsal attention network, and SN primarily mediated the relationship between age and the increase in local efficiency. This pattern suggests a developmental trajectory in early childhood from global information integration to local information segregation, with the SN playing a pivotal role in this transformation. This study provides novel insights into the mechanisms by which early childhood brain functional development impacts information transmission efficiency through cross-community adjustments in functional networks.

Integration of information and communication technologies in the training of nurses and health technicians in the Marrakech-Safi region of Morocco

Integration of information and communication technologies in the training of nurses and health technicians in the Marrakech-Safi region of Morocco

Trust Based Active Game Data Collection Scheme in Smart Cities

The concept of a smart city is to equip sensors to various objects in urban life to monitor areas and collect sensing data, and make wise decisions based on the collected data. However, some malicious sensor devices may interrupt and interfere with data collection, leading to a reduction in the integrity and availability of information, thereby causing harm to Internet of Things(IoT) applications. Therefore, identifying the credibility of sensor nodes to ensure the credibility of data collection is a challenge. This paper proposes a trust based active game data collection (TAGDC) scheme to collect trust data in the IoT. This TAGDC scheme mainly includes the following parts: 1)An active trust framework plus evolutionary game theory is proposed to encourage high-energy sensors to send detection routes and quickly obtain sensor trust. 2)In order to balance the data security requirements of subnetworks, the number and frequency of detection routes required by subnetworks are estimated through mechanism modeling and fuzzy analytic hierarchy process. 3)The design focuses on the internal trust computing model in the region to evaluate the trust of nodes. The findings of the experiment demonstrate that the TAGDC scheme, as described in this research study, enhances the accuracy of identifying malicious nodes by 20%, reduces the required identification time by 40%, and improves the data collection success rate by 5%.

Application of building prefabricated diaphragm walls: Mechanics-carbon dual-control parametric segmentation decision-making framework

As a crucial innovation in building below-grade envelope structures, prefabricated diaphragm walls (PDWs) require the integration of multi-layer information for effective segmentation. Existing decision-making approaches rely on a single condition, leading to significant disparities between decision outcomes and actual requirements. This study introduces the mechanic-carbon dual-control parametric segmentation decision-making (MCSD) framework for PDWs to integrate multi-layer information. MCSD framework firstly performs parametric characterization of PDW component shapes and local transport constraints. Refined numerical modeling is next employed to identify the dominant mechanical factors influencing segmentation decision-making. Optimization algorithm is developed to automatically generate feasible schemes. Finally, carbon assessment is utilized to identify optimal segmentation schemes. MCSD framework is verified by actual engineering and the following key findings are obtained. Compared to empirical methods, MCSD could quantitatively determine feasible segmentation positions across the entire surface of PDWs. Notably, MCSD reveals the competitive relationship between horizontal and longitudinal joints for the first time. By incorporating this mechanism for decision optimization, carbon dioxide equivalent could be reduced by up to 29.3%. Ultimately, MCSD transcends the traditional boundary between mechanics and carbon, enabling automated planning of PDW segmentation. This study provides scientific and quantitative guideline for the segmentation decisions of building prefabricated enclosure structures.

Lightweight Food Recognition via Aggregation Block and Feature Encoding

Food image recognition has recently been given considerable attention in the multimedia field in light of its possible implications on health. The characteristics of the dispersed distribution of ingredients in food images put forward higher requirements on the long-range information extraction ability of neural networks, leading to more complex and deeper models. Nevertheless, the lightweight version of food image recognition is essential for improved implementation on end devices and sustained server-side expansion. To address this issue, we present Aggregation Feature Net(AFNet), a lightweight network that is capable of effectively capturing both global and local features from food images. In AFNet, we develop a novel convolution based on a residual model by encoding global features through row-wise and column-wise information integration. Merging aggregation block with classic local convolution yields a framework that works as the backbone of the network. Based on the efficient use of parameters by the aggregation block, we constructed a lightweight food image recognition network with fewer layers and a smaller scale, assisted by a new type of activation function. Experimental results on four popular food recognition datasets demonstrate that our approach achieves state-of-the-art performance with higher accuracy and fewer FLOPs and parameters. For example, in comparison to the current state-of-the-art model of MobileViTv2, AFNet achieved 88.4% accuracy of the top-1 level on the ETHZ Food-101 dataset, with similar parameters and FLOPs but 1.4% more accuracy. The source code will be provided in supplementary materials.

Evaluation and Assessment of Software Security Risks and Vulnerabilities Within the Realm of Secure DevOps.

Security plays a vital role in successful software development and consistency, especially in a modern, fast-expanding world dominated by technologies. It guarantees that all privacy, integrity, and accessibility of information, coding, and services are protected. Achieving such requires a consistent consideration of security throughout all developmental stages of software. Although several methods for improving the quality of software have been created, there is still no clear method for effectively integrating security throughout each stage of the software developmental process. The inability to give adequate attention to security has been identified as the main cause of common flaws. Many forward-thinking businesses frequently employ a "patch and infiltrate” technique, taking security into consideration only after the execution of the task. Higher costs, project delays, neglect of verification and integration during the development life cycle of the project, reliance on external tools and parts, and ignorance among all factors contribute to the disregard of security during the SDLC. Nevertheless, Secure-DevOps is indispensable for the survival of companies in the Information and Communication Technology (ICT) landscape. Consequently, applying an effective approach that deals with security within every level of the SDLC is crucial. To bridge this gap, a comprehensive examination of effective techniques for developing secure software was presented, taking into account the financial and time constraints of a project. A framework for ensuring security built upon Secure-DevOps practices, which seamlessly integrates the best security measures into different stages of the SDLC, was identified. To validate this framework, a mathematical model has been employed. The study concludes that effectively incorporating Secure-DevOps practices throughout the Software developmental process, minimizes security risks and vulnerabilities.

CBHQD: A channel state information-based passive line-of-sight human queue detection

As the number of monitored individuals rises and multipath effects disrupt signals, existing queue monitoring solutions fail to meet efficiency needs. This paper proposes a passive line-of-sight (LOS) human queue detection method based on channel state information (CSI), namely CBHQD. We present a novel time series crowd detection network (TSCD-Net), incorporating genetic algorithm, LSTM, and FC layers to automatically extract amplitude and phase features from CSI, enhancing the simulation of indoor conditions. The genetic algorithm effectively addresses the challenge of local optima, while the fully connected layers excel in dimension reduction, facilitating the integration of valuable information obtained from LSTM. Additionally, we design the Fresnel zone detection, merging the Fresnel zone model with WiFi to estimate people's walking direction, thereby maximizing the accuracy performance of crowd detection. Lastly, we validate the feasibility and efficiency of our approach in a realistic testbed, demonstrating its suitability for detecting larger numbers of individuals.

A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps

Essential service water pumps are necessary safety devices responsible for discharging waste heat from containments through seawater; their condition monitoring is critical for the safe and stable operation of seaside nuclear power plants. However, it is difficult to directly apply existing intelligent methods to these pumps. Therefore, an intelligent condition monitoring framework is designed, including the parallel implementation of unsupervised anomaly detection and fault diagnosis. A model preselection algorithm based on the highest validation accuracy is proposed for anomaly detection and fault diagnosis model selection among existing models. A novel information integration algorithm is proposed to fuse the output of anomaly detection and fault diagnosis. According to the experimental results of modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) is selected, and a support vector machine using mean fusion processing multi-channel data (SVM (fusion)) is selected. The overall test accuracy and false negative rate of AKPCA (fusion) are 0.83 and 0.144, respectively, and the overall test accuracy and f1-score of SVM (fusion) are 0.966 and 1, respectively. The test results of AKPCA (fusion), SVM (fusion), and the proposed information integration algorithm show that the information integration algorithm successfully avoids a lack of abnormal status information and misdiagnosis. The proposed framework is a meaningful attempt to achieve the intelligent condition monitoring of complex equipment.