Network Tools Research Articles

Network Analysis of Psychological Empowerment, Need for Cognition, and Academic Self-Efficacy among Graduate Students.

The present study employed network analysis to explore the interrelationships between academic self-efficacy, psychological empowerment, and the need for knowledge at the symptom level among graduate students. Three hundred fifty-three graduate students from King Faisal University, Hofuf, Saudi Arabia (63.5% male, 72.2% in the 25-35 age range) completed the Psychological Empowerment Scale, Need for Cognition Scale (NCS), and Academic Self-Efficacy Scale as self-report measures. Different R-Studio programming software packages, such as "graph," "network tools," and "botnet," were used to analyze the current study's data. The most central node on the network analysis was Self.2 (history of success/failure), with the greatest betweenness (2.18), closeness (1.84), and strength (1.86). This node showed that it was essential for connecting other variables, despite having an expected negative effect (-0.37). Then there was also Psy.4 (impact), which showed a high centrality of betweenness 1.13 and closeness 1.29. Indeed, positive edges for Self.2 (previous successes/failures) and MB (Need for Cognition) (edge weight 0.37) clearly showed that good past experiences increased cognitive activity. A Bridge Betweenness of 10 was the most significant bridge node (Self.4 (university behavior) in the bridge analysis), linking network elements. Bootstrapped confidence intervals proved that these connections remained stable, ensuring the network was robust over resamples. Enhancing graduate students' self-efficacy and psychological empowerment, particularly through positive past experiences, could improve their academic performance and cognitive engagement. Future research should explore the implications of these findings for designing interventions to foster academic success.

Systematic application of saliency maps to explain the decisions of convolutional neural networks for glaucoma diagnosis based on disc and cup geometry

This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.8331 to 0.8890, compared to 0.8090 to 0.9203 for networks trained on the original images. The study used a dataset of 606 images, along with RIM-ONE DL and REFUGE datasets, and explored nine saliency methods. A discretization algorithm was applied to reduce noise and compute normalized attribution values for standard eye fundus sectors. Consistent with other medical imaging studies, significant variability was found in the attribution maps, influenced by the method, model, or architecture, and often deviating from typical sectors experts examine. However, globally, the results were relatively stable, with a strong correlation of 0.9289 (p<0.001) between relevant sectors in our dataset and RIM-ONE DL, and 0.7806 (p<0.001) for REFUGE. The findings suggest caution when using saliency methods in critical fields like medicine. These methods may be more suitable for broad image relevance interpretation rather than assessing individual cases, where results are highly sensitive to methodological choices. Moreover, the regions identified by the networks do not consistently align with established medical criteria for disease severity.

Optimal Feedback Rate for Multi-Antenna Maximum Ratio Transmission in Single-User MIMO Systems with One-Bit Analog-to-Digital Converters in Dense Cellular Networks

Stochastic geometry has emerged as a powerful tool for modeling cellular networks, especially in dense deployment scenarios where inter-cell interference is significant. Previous studies have extensively analyzed multi-antenna systems with partial channel state information at the transmitter (CSIT) using stochastic geometry models. However, most of these works assume the use of infinite-resolution analog-to-digital converters (ADCs) at the receivers. Recent advances in low-resolution ADCs, such as one-bit ADCs, offer an energy-efficient alternative for millimeter-wave systems, but the interplay between limited feedback and one-bit ADCs remains underexplored in such networks. This paper addresses this gap by analyzing the optimal feedback rate that maximizes net spectral efficiency in dense cellular networks, modeled using stochastic geometry, with both limited feedback and one-bit ADC receivers. We introduce an approximation of the achievable spectral efficiency to derive a differentiable expression of the optimal feedback rate. The results show that while the scaling behavior of the optimal feedback rate with respect to the channel coherence time remains unaffected by the ADC’s resolution, the absolute values are significantly lower for one-bit ADCs compared to infinite-resolution ADCs. Simulation results confirm the accuracy of our theoretical approximations and demonstrate the impact of ADC resolution on feedback rate optimization.

Antinuclear Positivity and Malignant Transformation Potential of Oral Potentially Malignant Disorder.

This multicenter cohort study aimed to investigate the impact of antinuclear antibody (ANA) positivity on the malignant transformation potential of oral potentially malignant disorders (OPMDs) using the Observational Health Sciences and Informatics network tools for the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Retrospective cohort data from five medical centers were analyzed. Cox regression and 1:4 propensity score matching followed by aggregated meta-analysis were used to evaluate association between ANA positivity and the risk of oral squamous cell carcinoma (OSCC). The index date was defined as the first day of diagnosis of OPMD. The target and comparator cohorts comprised patients who tested positive and negative for ANA within 180 days before or after the index date, respectively. Among 27,984 OPMD patients, 1302 patients (4.65%) confirmed malignant transformation. Among these, 3035 patients had ANA laboratory data. After 1:4 PS matching, 523 and 1598 patients with positive and negative for ANA were analyzed. Meta-analysis of the hazard ratios demonstrated that ANA positivity significantly increased the risk of malignant transformation compared to ANA negativity. ANA positivity is a significant prognostic marker for predicting malignant transformation in patients with OPMD.

Systematic Development of a Machine Learning-Based Asset Management Tool for Wastewater Pipeline Networks

Systematic Development of a Machine Learning-Based Asset Management Tool for Wastewater Pipeline Networks

Mignette: An R package for creating and visualizing migratory network models

Abstract A prominent challenge for managing migratory species is the development of conservation plans that accommodate spatiotemporally varying distributions throughout the year. Migratory networks are spatially‐explicit models that incorporate migratory assignment and seasonal abundance data to define patterns of connectivity between stages of the annual cycle. These models are particularly useful for widespread application because different types of migratory data can be used to quantify individual and population‐level movement across the annual cycle of migratory species. While there are clear benefits of combining migratory assignment and abundance data for the development of conservation strategies, there is a concurrent need for corresponding user‐friendly software to facilitate the integration of these data for conservation. Here, we present mignette (migratory network tools ensemble), an R package for developing migratory network models to estimate network connectivity among migratory populations. We demonstrate the functionality of mignette with three empirical examples that highlight the use of different types of tracking data for migratory assignment. mignette facilitates the modelling of migratory networks by providing R functions to: (1) define breeding and nonbreeding nodes, (2) assemble abundance and assignment data and (3) model the migratory network. Additionally, mignette provides R functions to visualize modelled migratory networks. With increasing availability of migratory assignment and abundance data, mignette represents a valuable tool for developing effective conservation strategies for migratory species.

A novel relaxed modified pipe hydrodynamic model for mixed flow simulation

In this study, we propose a novel modified pipe hydrodynamic model with a partial relaxation approach and develop a corresponding numerical algorithm within the framework of the finite volume method for its computation to enable robust, accurate, and comprehensive pipe modeling. The proposed numerical model provides some key improvements compared to existing numerical schemes for simulating mixed pipe flows: (1) it is capable of preserving both moving- and stationary-water steady states, and (2) it effectively reduces post-bore oscillations while maintaining second-order accuracy in the non-pressurized regime for unsteady pipe flows. Such improvements have been verified against theoretical and experimental data through tests involving both smooth and sharp-gradient steady and unsteady flows across various regimes in straight and locally bent circular pipes, which are fundamental elements of stormwater drainage, sewage networks, and industrial process pipelines. The proposed pipe flow model can be further improved regarding post-pore stability by utilizing specific post-bore oscillation suppression techniques, and it can be integrated into a practical hydraulic modeling tool for pipeline networks in future work.

Resilience patterns in the analysis of social-ecological networks of small-scale fishing in a subtropical estuarine system

Network analysis concepts and tools are being increasingly applied to Social-ecological systems (SES) in human-environment and sustainability sciences. In this study, we used the Gao metric (βeff) to measure the resilience of networks in small-scale fisheries (SSF) in the Paranaguá Estuarine Complex (PEC). The purpose was to evaluate the variations and trends in network resilience across eight hydrological sectors of the PEC during different seasons. Our analysis revealed that the βeff metric varied significantly across the eight SES sectors, suggesting that resilience is not uniformly distributed. Notably, the sectors located internally the estuary exhibited lower resilience values than the others. The average trophic level of the fishery catches was the higher hierarchical position variable that most affected the network topological metrics, followed by variables magnitude of conflicts, water and sediment quality, and rainfall. Our hydrodynamic sectorization, reflecting ecological and social interactions, enabled us to identify distinct interaction networks within the PEC. This shows the complexity of managing these SES to ensure the sustainability and resilience of the system. While other variables and spatial and temporal scales could lead to different estimates and relationships, our findings shed light on the importance of resilience quantification and the interaction networks within SES to manage them effectively.

Research trends and hotspots in clinical trials of migraine in the past 20 years: bibliometric analysis.

Migraine is a widespread, recurrent primary headache disorder primarily characterized by severe pulsatile headache, typically on one or both sides. It is often accompanied by nausea, vomiting, and hypersensitivity to sound and light. Despite the availability of multiple drugs for migraine management, the condition often becomes chronic due to untimely or irrational drug use, significantly distressing patients and increasing the burden on families and society. Over the past two decades, numerous clinical studies on migraine have been published. This study aimed to provide a comprehensive summary of the current status and trends of migraine clinical trials through bibliometric analysis. We used visual network tools such as CiteSpace and VOSviewer to perform a knowledge graph analysis of publications related to migraine clinical trials extracted from the WoSCC. This study analyzed 1,129 articles published in 389 journals from 61 countries. The number of publications on migraine clinical trials has steadily increased from 2004 to 2023. The United States and Albert Einstein College of Medicine are the leading countries and institutions in this field, respectively. Richard B. Lipton is the most prolific author, making significant contributions to the research. The journal Headache has the highest number of publications and citations in this area. Keywords such as "efficacy," "RCT," "CGRP," "prophylaxis," "disability," "depression," "questionnaire," and "real-world effectiveness" received significant attention. This study identified reliable research hotspots and provided directions for clinicians. The treatment of migraine continues to be challenging. Future trends may include continued growth in migraine classification, risk factor analysis, and comorbidity studies. Research on CGRP and epigenetics will advance the progress of precision medicine in the migraine field.

Network Tools for Building Trust in the Modern Transforming Economic Relations (Using the Example of the Sharing Economy in the city of Moscow)

Network Tools for Building Trust in the Modern Transforming Economic Relations (Using the Example of the Sharing Economy in the city of Moscow)

Testing the fidelity of zircon as a provenance indicator in fluvial‐fan successions: An example from the Palaeogene Colton Formation, Central Utah, USA

AbstractThe stratigraphic, spatial and temporal variability of detrital zircon age populations in continental sedimentary successions is a critical tool in understanding palaeodrainage networks and how these systems distributed detritus within sedimentary basins. However, multiple factors, such as variations in sediment‐transport processes, the scale of the depositional environment and the architecture of the sedimentary succession are often overlooked in detrital zircon studies. This article presents detrital zircon U–Pb geochronology from the fluvial‐dominated Colton Formation in the western Uinta Basin (Utah, USA) to assess the system's provenance and evolution. Significant differences in zircon age populations between the Colton Formation and the overlying Green River Formation suggest a reorganisation of the source‐to‐sink system during the transition between the two lithostratigraphic units. Notably, detrital zircon age spectra are not homogeneous across the Colton Formation, therefore physical morphometric parameters were used to verify the possible influence of selective bias during sediment transport. These data reveal that a relatively finer‐grained population of Precambrian, and to a less extent Mesozoic, zircon grains were affected by hydraulic sorting during transport, resulting in a greater relative abundance of older zircon grains in the distal reaches of the distributive fluvial system, whose basinward decrease in competence would have increased the relative proportion of finer zircon fractions in sandstones. Furthermore, there are different trends in the distributions of zircon age populations relative to their stratigraphic position, highlighting the complex architecture of the fluvial palaeo‐fan. The spatial and stratigraphic variability of provenance signals in fluvial‐fan successions must be carefully evaluated to improve the reliability of source‐to‐sink models and palaeodrainage reconstructions, as autogenically controlled noise can be generated during the dispersal of detrital zircon in fluvial sedimentary systems.

Frequency tuning of a squeezed vacuum state using interferometric enhanced acousto-optic effect.

Quantum frequency conversion is one of the important tools for quantum information processing. So far, the frequency conversion of continuous variable quantum state in radio frequency range has not been demonstrated yet. Here, we experimentally demonstrate the optical frequency fine tuning of a squeezed vacuum state by using an acousto-optic modulator based bi-frequency interferometer. The systematic efficiency of the frequency tuning device is 91%, which is only confined by the optical transmission efficiency of the acousto-optic modulators. The amount of frequency tuning is 80 MHz, which is orders of magnitude larger than the line-width of the laser used to generate the squeezed state and can, in principle, be further extended. A squeezed vacuum state with -3.47 ± 0.02 dB squeezing level is sent to the frequency tuning device, and a squeezing level of -1.98 ± 0.02 dB is directly measured by a fiber coupled homodyne detector after the frequency tuning. Our scheme can also be applied to a variety of other quantum optical states as well and will serve as a handy tool for quantum networks.

Machine learning-based modeling of surface water temperature dynamics in arctic lakes.

Lake surface-water temperature (LSWT) regulates physical and biochemical processes in lakes. Therefore, understanding the LSWT dynamics is important, especially in Arctic zone since the region is experiencing a warming rate that is greater than the Earth's average. However, regular measurements of LSWT in the remote Arctic lakes always face difficulties or cannot be done by satellites accurately due to the cloud cover and their limited spatiotemporal resolution. Here, we used a historically rich data (1960-2023) to develop four machine learning-based algorithms for the daily LSWT modeling in Lake Inari, situated in Arctic zone, using the air-temperature data. Our results showed that both air-temperature (0.030 °C/yr) and LSWT (0.023m °C/yr) were warming with a rate faster than those in the globe. The long-short-term memory model, with the coefficients of determination varied from 0.96 to 0.98, outperformed other algorithms in modeling of the daily LSWT dynamics in Lake Inari, followed by both support vector regression and neural network tools, and random forest model. As the air-temperature data are widely accessible through synoptic stations and remote sensing techniques, our suggested models can be simply adopted for other Arctic lakes, where the local water-temperature data are often lacking or contain large windows of missing data due to harsh atmospheric conditions and equipment failure.

Using neural network tools to accelerate the development of Web interfaces

Abstract. The article is devoted to considering modern neural network tools that allow for speeding up the development of web interfaces and simplifying the work of UI/UX designers. One of the main problems of modern design is quick access to general information and possible structuring of a site with specialized content, as well as obtaining its visual content. Currently, neural networks cannot replace designers, but to a large extent help them solve tasks. All neural networks that can be used in the design of web interfaces can be divided into four main types: convolutional, recurrent, forward propagation, and generative adversarial networks. In his work, the designer can mainly use generative networks, they can be classified according to the principle of "information at the input - information at the output". When working on a project, the designer can create a request to the neural network and get several options, generate different ideas, and create mood boards based on them, selecting colors, gradients, texture, typography, etc. The neural network can create various graphic elements: icons, buttons, illustrations, and photos with the right perspective, style, and colors. Using neural networks to improve images and refine or remove necessary elements is also promising. The process of speeding up the creation of the landing page interface using the Midjourney application is considered. Examples of writing prompts (prompts) that will affect the final quality of the generated image are given. The results are high-quality visual content that can either be placed in a project or used as an idea, element placement, composition, color scheme, photos, icons, etc. After creating the graphic design elements using Chat GPT 3.5, the landing page's content was created. You can use the FIG GPT plugin directly in the Figma environment to quickly generate the required content. Existing shortcomings and generation inaccuracies that arise in the work can be corrected by quickly updating and creating new versions of neural networks.

Tisserand-Leveraging Lambert Problem: Formulation, Solution, and Applications

Tisserand-leveraging transfers (TILTs) were proposed as techniques to achieve an endgame tour with small orbit insertion maneuvers and short flight times in low-energy regions of the planar, circular, restricted three-body problem. This paper presents a new formulation of TILTs with a framework similar to the classical Lambert problem. The developed algorithm searches for trajectories of TILTs with constraints on boundary values and time of flight by solving a one-dimensional root-finding problem. Compared to the traditional algorithm for TILTs, the developed algorithm enhances its generality by removing the restriction on the initial and final phases of the transfer. Additionally, the algorithm can achieve trajectory patching that includes TILTs without reoptimization and tour redesign based on the actual position of the spacecraft due to the Lambert-like input–output design. The algorithm uses existing neural network tools for fast and broad search in the solution space. Simulation results indicate that the algorithm can explicitly find TILT solutions and families satisfying boundary value constraints. Furthermore, the algorithm is applied to design a new endgame trajectory from Ganymede to Europa, demonstrating the potential of integrating the new algorithms with other trajectory design tools.

INFORMATION SYSTEM FOR ASSESSING THE LEVEL OF HUMAN CAPITAL MANAGEMENT

The article offers conceptual foundations for formalizing the process of assessing a level of human capital (HC) management at the enterprise using mathematical and computer modeling based on neural network technologies. The methodological approach for assessing the level of human capital management has been improved. This allows the use of neural network tools to identify accurately and reasonably the level of HC management with the help of self-learning multilayer perceptron. The weight coefficients of such a network were calculated. An appropriate artificial neural network – a multilayer perceptron – was built using the mathematical software MatLab and it was successfully diagnosed. The improved mathematical model for assessing the level of HC management at the enterprise makes it possible to display transparently a set of input parameters on a set of output solutions, to decompose such a process, and to simplify the procedure of its formalization. The designed neural network allows us to determine quickly and accurately the level of HC management at the enterprise. The conceptual approach proposed by the authors has several significant advantages over existing alternative methods: accuracy of assessment; taking into account a wide range of various evaluation parameters of impact; high speed of making decisions and self-learning ability. The proposed approach was successfully implemented to assess the level of HC management at 24 domestic enterprises. The information system "HC" developed by the authors allows to calculate the estimated parameters of the evaluation process; to determine the level of HC management based on the mathematical apparatus of the multilayer perceptron. Such estimates correlate with the estimates obtained by the experts of these enterprises which indicates the adequacy of the approach proposed by the authors. Therefore, the proposed information system for assessing the level of management of the HC allows accurate implementation of such a process with minimal time and money costs.

Abstract B164: Sustaining statewide transdisciplinary collaborations to address cancer disparities

Abstract Background: Wisconsin’s cancer disparities are evident when considered by race/ethnicity, geography, and socio-economic status. Addressing these disparities requires deeper integration of social and scientific problem-solving and a systems approach. Given the nature of disparities, it also demands that we not just seek equitable outcomes but collaborate in ways that reflect equity in the process. To achieve this, we developed a transdisciplinary approach inclusive of community, clinicians, and researchers and builds capacity to understand, at a deep level, factors that underpin disparities and design newer sustainable solutions. Methods: The Community and Cancer Science Network (CCSN) Integration Hub was established within the Medical College of Wisconsin Cancer Center to provide leadership and capacity building to eight transdisciplinary teams working to address cancer disparities. The Integration Hub and its teams use a co- leadership structure with a community and academic PI as well as a facilitator and evaluator. The Integration Hub has four primary strategies: identifying and addressing knowledge gaps in cancer disparities, building capacity for transdisciplinary collaboration, cultivating networks and relationships, and disseminating through feedback loops that touch communities, academic medicine, and policy. Our approach is grounded in the principles of deep equity, considerations across the biology to policy spectrum, and systems thinking. Results: In its third year, CCSN adapted and adopted tools to understand its effectiveness in building a collaborative environment for transdisciplinary approaches to problem-solving and to set strategy for sustainability. Results from a partner survey administered to team members (n=73) highlighted that the CCSN model contributes to fostering learning conditions that support a strong transdisciplinary approach including a supportive learning environment, concrete learning practices, leadership that reinforces learning and a reflection of core principles as well as embedding a transdisciplinary orientation in team members. The Integration Hub leadership also completed the Organizational Mapping Tool for Coalitions, Alliances and Networks (OMT-CAN) to assess critical dimensions of network health. The assessment helps identify strengths and identify areas for improvements. CCSN network leaders (n=21) rated the network moderate to strong in culture and relationships; joint action, planning, learning and evaluation; and in purpose, goals and strategy. The team further identified the need to work on refining structure, identity and participation; its capacity for strategic communications; and advocacy and organizing. Discussion: To integrate social and scientific problem-solving and address cancer disparities requires the resourcing of credible capacity building networks that can broker equitable relationships between academic medicine and community members and organizations. The CCSN Integration Hub offers a strong model embedded within a cancer center that accomplishes those goals. Citation Format: Tobi Cawthra, Laura Pinsoneault, Kristen Gardner-Volle, Alexis Krause, Kim Kinner, Melinda Stolley. Sustaining statewide transdisciplinary collaborations to address cancer disparities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B164.

Storying connectivity and value: the south west creative technology network’s cultural ecologies as network visualisations

ABSTRACT This paper explores the productive connectivity developed by university-industry knowledge exchange (KE) programmes for research and development (R&D) and their emergent cultural value. The South West Creative Technology Network (SWCTN) is our case study. We build on Dovey et al.'s. work to visualise the cultural ecology supported and created by cohort-led R&D programmes. Our visualisations, combined with interview data, evidence that new connections create diverse forms of value, including inspiration, mentoring, training, and recruitment opportunities, and cooperation through collaborative working. We supplement social network and visualisation tools with rich qualitative data to explore the “why,” “how” and “to what end” of new connections, contributing to the field of Mixed Methods in Social Network Analysis (MMSNA). This methodology is novel to the field of creative economy research. We argue for new forms of R&D funding for the creative sector to take a cultural ecology approach if we wish to take seriously and invest in the diverse forms of value beyond economic drivers, sometimes described as “intangible,” that play an important role in building regional capacity, innovation and productivity.

A Mobile-Based Network Tools Information System to Enhanced Connectivity and Security

This paper addresses the common challenges faced by network administrators in managing and troubleshooting network issues. Traditional desktop-based network management tools often restrict mobility and responsiveness, hindering the ability to address network problems promptly. To address these limitations, this study presents the development of a mobile-based network tools information system. The proposed solution integrates various network management functionalities into a mobile application, enabling administrators to monitor, diagnose, and resolve network issues remotely. Employing a design and implementation approach, the research includes user requirements analysis, system design, and rigorous testing. Results show significant improvements in response times and diagnostic accuracy. User feedback highlights high satisfaction with the system's functionality and ease of use. Overall, the mobile-based network tools information system offers a versatile and effective solution for network management, enhancing operational efficiency and responsiveness in diverse network environments.

GAP-LSTM: Graph-Based Autocorrelation Preserving Networks for Geo-Distributed Forecasting.

Forecasting methods are important decision support tools in geo-distributed sensor networks. However, challenges such as the multivariate nature of data, the existence of multiple nodes, and the presence of spatio-temporal autocorrelation increase the complexity of the task. Existing forecasting methods are unable to address these challenges in a combined manner, resulting in a suboptimal model accuracy. In this article, we propose GAP-LSTM, a novel geo-distributed forecasting method that leverages the synergic interaction of graph convolution, attention-based long short-term memory (LSTM), 2-D-convolution, and latent memory states to effectively exploit spatio-temporal autocorrelation in multivariate data generated by multiple nodes, resulting in improved modeling capabilities. Our extensive evaluation, involving real-world datasets on traffic, energy, and pollution domains, showcases the ability of our method to outperform state-of-the-art forecasting methods. An ablation study confirms that all method components provide a positive contribution to the accuracy of the extracted forecasts. The method also provides an interpretable visualization that complements forecasts with additional insights for domain experts.