Network Data Collection Research Articles

Financial stability correlates with happiness for retired veterinarians.

To gain an understanding of the perceptions and experiences of retired veterinarians. An invitation to participate in an electronic survey distributed via the Veterinary Information Network data collection portal was sent to veterinary members of the Veterinary Information Network who identified as retired. Data were collected between August 15, 2024, and September 14, 2024. We received 512 responses from veterinarians who indicated they had retired. Participants rated a desire for more free time, having sufficient financial security, and a desire to maintain good health as the most important factors in their decision to retire. The most frequently endorsed negative aspects of retirement included concerns about clients and establishing a routine. The most frequently endorsed positive aspects included feeling more relaxed and being able to do more of what they enjoy. A higher percentage of females endorsed several positive aspects of retirement than males. The results suggested that most retired veterinarians adjust well to retirement and reap the positive aspects of a retired lifestyle. A minority, however, struggle. To help ensure everyone is able to transition well and enjoy their retirement years, veterinary organizations should consider offering retirement-related guidance and resources, similar to those offered to new graduates entering the field. As an increasing number of veterinarians approach retirement age, it is paramount that veterinary organizations help support veterinarians by offering retirement mentorship programs, resource toolkits and educational webinars/presentations, and financial guidance.

Joint Clustering and 3-D UAV Deployment for Delay-Aware UAV-Enabled MTC Data Collection Networks

Joint Clustering and 3-D UAV Deployment for Delay-Aware UAV-Enabled MTC Data Collection Networks

Energy efficient mobile sink driven data collection in wireless sensor network with nonuniform data

In wireless sensor network, mobile sink is used to collect data from sensor nodes by periodically traversing the network to prevent hotspot problem. However, when sensor nodes generate data non-uniformly, the efficiency of data collection is constrained by rendezvous points and network topology. It becomes more challenging under network energy consumption constraint. Thus, this paper investigates non-uniform data generation in wireless sensor network and proposes an innovative approach: Optimal Clustering and Network Topology for Mobile Sink-Driven Data Collection, called OCNTMS. It focuses on determining optimal rendezvous points and their associated clusters. Through innovative methods such as weight-balanced clustering, cost function optimization, load-balanced link construction, and node forwarding selection, the OCNTMS can efficiently construct link sets within clusters and accurately plan the mobile sink’s traversal of rendezvous points. Simulation results show that the OCNTMS reduces energy consumption by 18% and increases network lifetime by 40% compared with existing approaches under the constraint of non-uniform data generation. This greatly improves the network energy efficiency and data transmission efficiency.

AI-Empowered Intelligent Search for Path Planning in UAV-Assisted Data Collection Networks

AI-Empowered Intelligent Search for Path Planning in UAV-Assisted Data Collection Networks

Using Social Network Analysis to Identify Influential Community Organizations

BackgroundWe used a social network approach to identify influential community organizations within integrated public health networks in three regions in Texas, US: Houston/Harris County, Cameron County, and Northeast Texas. The study aimed to understand how network structures influence organizational performance in COVID-19 response. MethodsIn collaboration with community partners in each region, we administered a snowball sampling network data collection. Initial seed sets for the regions of 23, 25, and 55 organizations identified other organizations that then responded to the survey, which expanded the sets to 268, 145, and 138, respectively. Principal component analysis (PCA) identified key network structures—centrality and brokerage—and we introduced two network-based performance measures: Fulfillment Rate (FR), which measures securing needed resources, and Provision Rate (PR), which measures providing resources to others. We tested hypotheses to examine the associations between these network structures and organizational performance. FindingsCentrality consistently emerged as a predictor of organizational performance across all three regions. Community organizations that are more centrally positioned, rather than broker organizations, play a critical role in meeting resource needs and providing services to other organizations. Centrally positioned organizations conducted more COVID-19 tests, whereas brokers administered fewer vaccines. InterpretationsThis study suggests that public health interventions should prioritize central organizations for resource allocation and distribution. Our network-based performance metrics offer new avenues for evaluating organizational effectiveness in networked public health systems, with broader applicability to future crises and public health planning.

Large language models recover scientific collaboration networks from text

Science is a collaborative endeavor. Yet, unlike co-authorship, interactions within and across teams are seldom reported in a structured way, making them hard to study at scale. We show that Large Language Models (LLMs) can solve this problem, vastly improving the efficiency and quality of network data collection. Our approach iteratively applies filtering with few-shot learning, allowing us to identify and categorize different types of relationships from text. We compare this approach to manual annotation and fuzzy matching using a corpus of digital laboratory notebooks, examining inference quality at the level of edges (recovering a single link), labels (recovering the relationship context) and at the whole-network level (recovering local and global network properties). Large Language Models perform impressively well at each of these tasks, with edge recall rate ranging from 0.8 for the highly contextual case of recovering the task allocation structure of teams from their unstructured attribution page to 0.9 for the more explicit case of retrieving the collaboration with other teams from direct mentions, showing a 32% improvement over a fuzzy matching approach. Beyond science, the flexibility of LLMs means that our approach can be extended broadly through minor prompt revision.

Model Complexity Reduction for ZKML Healthcare Applications: Privacy Protection and Inference Optimization for ZKML Applications-A Reference Implementation With Synthetic ICHOM Dataset.

Web 3.0 represents the next significant evolution of the internet that embodies the underlying decentralized network architectures, distributed ledgers, and advanced AI capabilities. Though the technologies are maturing rapidly, considerable barriers exist to high-scale adoption. The author discusses the barriers and the mitigations through specific technologies maturing to solve those issues in an earlier paper titled Moving Beyond POCs and Pilots, published in 2023 in Blockchain in Healthcare Today. These include privacy-preserving technologies, off-chain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies. As an extension, this paper discusses one such enabler, zero knowledge machine learning (ZKML), which merges two streams of technology in unique ways to address problems in privacy and the cost of inference. Zero-knowledge proofs (ZKP) allow one party to prove the validity of a statement to another party without revealing any additional information about the statement itself. The ZKML combines the cryptographic principle of ZKP with machine learning (ML) techniques. It is still a maturing technology and needs baselines for applications in global healthcare. In this effort, the authors conceptualize the technical and operational feasibility of using ZKML and implement a reference healthcare implementation using the synthetic International Consortium for Health Outcomes Measurement (ICHOM) in the evaluation phase in a global healthcare setting for high-volume data collection, including patient-reported outcomes. Model complexity reduction is researched and reported for the ICHOM diabetes dataset to advance the usage of ML models in global standards of healthcare data collection in network decentralized architectures for increased data protection and efficiencies.

Application of Wireless Network Data Collection Based on Optical Topology Sensors in Sports Technology Evaluation

Application of Wireless Network Data Collection Based on Optical Topology Sensors in Sports Technology Evaluation

ATENA: Adaptive TEchniques for Network Area Coverage and Routing in IoT-Based Edge Computing

The Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.

Peers and value preferences among adolescents in school classes: a social network and longitudinal approach

The aim of our study was twofold: (1) to explore the role of value preferences on peer relations in school classes (selection effect) and (2) to explore the role of peers’ values on adolescents’ values (influence or socialization effect) in three types of networks (friendship, advice, and trust). To answer these questions, we used a longitudinal social network approach in a study of N = 903 adolescents (57% girls) from 34 secondary school classes in Poland. Pupils began participating in the study when they joined their secondary school and were followed over two and a half years. Panel data were collected at six measurement time points during this period. Values were conceptualized according to the values theory proposed by Schwartz and measured by the Portrait Value Questionnaire. The collection of network data followed a roster design. Pupils were asked to evaluate the strength of their friendships, as well as the frequency with which they approached peers to ask for advice about school or homework or to talk about things that are important to them in the last 2 weeks. We found empirical support for both selection and socialization effects, especially for protection values (Conservation and Self-enhancement). The selection effect was most evident in advice and trust networks and the socialization effect was particularly prevalent in friendship and trust networks.

A mobile data collection method for balancing energy consumption and delay in strip-shaped wireless sensor networks with branches

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay.

Artificial Neural Networks as Efficient Models of Proton Exchange Membrane Fuel Cells

Abstract Utilization of machine learning methodologies, particularly artificial neural networks (ANNs), presents a good approach to accurately model physical systems. Such predictive simulations offer the ability to predict system performance across diverse operational conditions without the need of use mathematical descriptions. This approach contrast with traditional, time-consuming and unstable approaches reliant on partial differential equations. In this study, ANN methodology is used to obtain the characteristics of proton exchange membrane fuel cells (PEMFCs). PEMFC are low temperature devices which convert chemical energy into electricity. This devices are promising applications in the automotive sector. Utilizing data gained from computational fluid dynamics simulations of PEMFCs, was explored various data collection techniques and network architectures to check their impact on predictive fidelity. Conclusions show that the ANN-based framework enables rapid prediction of current-voltage characteristics, achieving accuracy levels surpassing 90%. Practical of machine learning model implications were discussed, accenting its utility in optimizing PEMFC operational parameters and its potential integration within digital twin frameworks as a data-driven surrogate model. This study underscores the efficiency of machine learning techniques in advancing the comprehension and optimization of complex physical systems such as PEM-FCs, thereby paving the way for their use in engineering and energy sectors.

Optimization of UAV Flight Paths in Multi-UAV Networks for Efficient Data Collection

Optimization of UAV Flight Paths in Multi-UAV Networks for Efficient Data Collection

LSTM Based New Probability Features Using Machine Learning to Improve Network Attack Detection

This project focuses on improving the detection of network attacks by using a machine learning technique known as Long Short-Term Memory (LSTM) networks. LSTM networks are a type of neural network that excels at analyzing sequences of data, making them well-suited for identifying patterns associated with network intrusions. To enhance the LSTM model's effectiveness, we introduce new probability features that help the model better distinguish between normal and malicious activities. Our approach includes collecting network data, preprocessing it to make it suitable for training, and then using this data to train the LSTM model. We evaluate the model's performance using a range of metrics to ensure its accuracy and reliability. The results indicate that our method significantly improves the detection rate of network attacks while also reducing the number of false alarms. This means that our LSTM-based model not only catches more real threats but also makes fewer mistakes in identifying normal activities as attacks. Overall, this project showcases the potential of advanced machine learning techniques, like LSTM networks, to enhance cyber security measures and protect against network threats more effectively.

Advanced Moored Data Buoys for Catching Typhoons in the Western North Pacific

Abstract With the western North Pacific being one of the most active typhoon hotspots, a reliable buoy system is essential for in situ observations during typhoon passages. This study details the development and implementation of two improved buoys designed by National Taiwan University based on the Autonomous Temperature Line Acquisition System. It aims to establish a buoy network for near real-time, high-resolution meteorological and oceanic data collection during typhoon periods in the western North Pacific. The buoy system was distinguished by its reliable design, low power-consumption system, dual satellite and radio communication system, and inductive modem-based data acquisition system. Moreover, the system was proven robust to extreme weather conditions and fishing challenges. From 2015 to 2022, we conducted sea trials in typhoon hotspot regions every summer (normally June to October), demonstrating the buoys' durability and capability to withstand 21 typhoons while collecting valuable scientific data.

Potentially Better Practices for Follow Through in Neonatal Intensive Care Units.

To ascertain how NICU teams are undertaking action to follow through, involving teams, families, and communities as partners to address health-related social needs of infants and families. Nineteen potentially better practices (PBPs) for follow through first published in 2020 were reported and analyzed as a sum, overall, and by safety-net hospital status, hospital ownership, and NICU type, among US NICUs that finalized Vermont Oxford Network data collection in 2023. One hundred percent of 758 eligible hospitals completed the annual membership survey, of which 57.5% reported screening for social risks. Almost all NICUs offered social work, lactation support, and translation services, but only 16% included a lawyer or paralegal on the team. Overall, 90.2% helped families offset financial costs while their infants were in the hospital, either with direct services or vouchers. At discharge, 94.0% of NICUs connected families with appropriate community organizations and services, 52.9% provided telemedicine after discharge, and 11.7% conducted home visits. The median number of PBPs at each hospital was 10 (25th percentile: 8, 75th percentile: 12). The number of PBPs reported differed by hospital control or ownership and level of NICU care. There were no differences by safety-net hospital status. Despite concerns about time and resources, a diverse set of US NICUs reported adopting potentially better practices for follow through. However, the marked variation among NICUs and the lower rates at for-profit and lower-level NICUs suggest there is substantial opportunity for improvement.

Revolutionizing digital healthcare networks with wearable strain sensors using sustainable fibers

AbstractWearable strain sensors have attracted research interest owing to their potential within digital healthcare, offering smarter tracking, efficient diagnostics, and lower costs. Unlike rigid sensors, fiber‐based ones compete with their flexibility, durability, adaptability to body structures as well as eco‐friendliness to environment. Here, the sustainable fiber‐based wearable strain sensors for digital health are reviewed, and material, fabrication, and practical healthcare aspects are explored. Typical strain sensors predicated on various sensing modalities, be it resistive, capacitive, piezoelectric, or triboelectric, are explained and analyzed according to their strengths and weaknesses toward fabrication and applications. The applications in digital healthcare spanning from body area sensing networks, intelligent health management, and medical rehabilitation to multifunctional healthcare systems are also evaluated. Moreover, to create a more complete digital health network, wired and wireless methods of data collection and examples of machine learning are elaborated in detail. Finally, the prevailing challenges and prospective insights into the advancement of novel fibers, enhancement of sensing precision and wearability, and the establishment of seamlessly integrated systems are critically summarized and offered. This endeavor not only encapsulates the present landscape but also lays the foundation for future breakthroughs in fiber‐based wearable strain sensor technology within the domain of digital health.

Healthcare social network research and the ECHO model™: Exploring a community of practice to support cultural brokers and transfer cultural knowledge

BackgroundProject ECHO® networks at Children’s Health Queensland Hospital and Health Service (CHQHHS) are communities of practice designed to mitigate services and systems fragmentation by building collaborative partnerships addressing priority child and youth health needs. Aboriginal and Torres Strait Islander people experience the negative impacts of fragmentation in addition to historical challenges of absent or culturally inappropriate health services. Access to culturally safe and responsive services can be improved by engaging Aboriginal and Torres Strait Islander Health Workers and similar roles in an online community of practice, supporting the integration of cultural and clinical knowledge and self-determination of Aboriginal and Torres Strait Islander consumers in decisions affecting their health.Analysing professional support networks and knowledge sharing patterns helps identify enablers and barriers to partnerships. Using social network research, the multilevel network inclusive of ECHO network members and their colleagues was studied to identify interdisciplinary and cross-sector advice exchange patterns, explore the position of cultural brokers and identify common relational tendencies.MethodsSocial network theories and methods informed the collection of network data and analysis of advice-seeking relationships among ECHO network members and their nominees. Registered members from two ECHO networks were invited to complete the Qualtrics survey. Networks analysed comprised 398 professionals from mainstream health, Aboriginal and Torres Strait Islander Community Controlled Health Organisation, education, disability and child safety service settings.ResultsBrokers were well represented, both those who hold knowledge brokerage positions as well as cultural brokers who incorporate clinical and cultural knowledge enabling holistic care for Aboriginal and Torres Strait Islander patients (38 individuals, 17% of network). Professionals who occupy brokerage positions outside the ECHO network tend to be more connected with co-members within the network.ConclusionsThis study is the first application of contemporary social network theories and methods to investigate an ECHO network. The findings highlight the connectivity afforded by brokers, enabling the coordination and collaboration necessary for effective care integration. Inclusion of cultural brokers in an ECHO network provides sustained peer group support while also cultivating relationships that facilitate the integration of cultural and clinical knowledge.

Actor-Critic-based UAV-assisted data collection in the wireless sensor network

Actor-Critic-based UAV-assisted data collection in the wireless sensor network

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms.