Network Data Research Articles

Disparities and longitudinal complications in opioid prescriptions after dermatologic surgery: An international propensity score-matched, retrospective cohort study.

In dermatology, the majority of opioid prescriptions occur in dermatologic surgery. Even short courses of opioids are linked to substance abuse. Reported rates of opioid prescription in dermatologic surgery are variable, with no consensus on when they should be prescribed. Little population data currently exists on opioids in dermatologic surgery and the potentially serious complications that result. Here, we use an international patient data network to evaluate trends in postoperative opioid prescription in the setting of dermatologic surgery and complications that may result. This retrospective cohort study analyzed patients within the TriNetX research network. A total of 1,160,223 dermatologic surgery patients were extracted. Risk analyses were conducted examining (1) oral opioid prescription within 2 days postsurgery; (2) adverse events related to opioid use 3 months to 5 years postsurgery. Propensity score matching included variables: age, gender, race, ethnicity, complex regional pain syndrome, back pain, osteoarthritis, and malignancy. A total of 124,292 (13.6%) patients received postsurgical opioids. Black or African American race, female gender, and Latino ethnicity were associated with significantly higher absolute risk of postsurgical opioid prescription. A positive history of prior opioid prescription, opioid abuse or dependence, or substance abuse yielded a significant increase in the absolute risk of receiving postsurgical opioids (P < 0.0001). Patients prescribed postsurgical opioids had a statistically significantly higher risk of subsequent oral opioid prescription (P < 0.0001), opioid abuse (P = 0.0005), substance abuse (P < 0.0001), overdose by opioids (P = 0.031), constipation (P < 0.0001), and chronic pain (P < 0.0001) 3 months to 5 years after surgery. This study finds female, African American, and Latino populations have a higher risk of being prescribed opioids postoperatively. This prescription may confer risks of potentially serious complications related to opioid use. Dermatologic surgeons should be aware of the risks these populations face when determining candidacy for postsurgical opioid analgesia.

Family networks of lesbian and gay people in Switzerland

AbstractObjectiveThe aim of this study was to estimate the relevance of the family of choice hypothesis in family networks of lesbian and gay individuals living in Switzerland and its implications for their social capital.BackgroundOver the past three decades, family scholars have paid more attention to the emergence of family configurations of LGBTQ+ people that extend beyond the nuclear family and blood kin, with voluntary kin and family of choice playing a key role. However, family networks of lesbian and gay individuals remain an unexplored topic in Switzerland, a country that has taken an extended period to implement institutional acknowledgment of lesbian and gay family rights.MethodPersonal network methods were used to map the main types of family networks of lesbian and gay individuals. In collaboration with an association advocating for lesbian and gay families in Switzerland, the study collected ego‐centered network data on their family.ResultsThe results reveal that the nuclear family holds prominence in many family networks of lesbian and gay people included in the sample, with limited involvement of either blood kin or voluntary kin, which has consequences for their family‐based social capital.ConclusionThe family of choice hypothesis is largely rejected. The focus on the nuclear family is consistent with the late legal recognition of same‐sex marriage and parenthood in Switzerland.ImplicationsThe importance of the nuclear family in the family networks of LGBTQ+ people, as well as the relative diversity of such networks, should be considered by professionals dealing with health and social issues, as well as by legislators, policymakers, and organizations working to promote the family rights of lesbian and gay people and their full social acceptance.

The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems

Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.

A time-variant power distribution network voltage sag identification method based on the concept of inheritance

The present paper introduces a novel method for identifying voltage sags in time-variant power distribution networks, effectively addressing the challenges arising from the temporal variability of network topology and data. The proposed method is founded on the concept of inheritance, which is bifurcated into breadth and depth inheritance strategies. The breadth inheritance strategy employs transfer learning to manage topological temporality, utilizing the Euclidean distance between samples to ascertain the sequence of sample migration, and implements multitask learning to share feature representations across different tasks. The depth inheritance strategy, on the other hand, utilizes incremental learning to handle data temporality, building upon the initial model parameters to learn new sample features, which in turn reduces the time required for model updates and enhances the accuracy of target tasks. Case study findings validate the suitability of the proposed methods for reconstructing fault identification models in scenarios characterized by topological temporal variability and for rapidly updating fault identification models in scenarios with data temporal variability. The approach presented herein holds significant implications for the enhancement of power supply reliability and the adaptability of electrical grids.

Energy-Efficient Wireless Multimedia Sensor Nodes for Plant Proximal Monitoring

The paper presents a double-radio wireless multimedia sensor node (WMSN) with a camera on board, designed for plant proximal monitoring. Camera sensor nodes represent an effective solution to monitor the crop at the leaf or fruit scale, with details that cannot be retrieved with the same precision through satellites or unnamed aerial vehicles (UAVs). From the technological point of view, WMSNs are characterized by very different requirements, compared to standard wireless sensor nodes; in particular, the network data rate results in higher energy consumption and incompatibility with the usage of battery-powered devices. Avoiding energy harvesters allows for device miniaturization and, consequently, application flexibility, even for small plants. To do this, the proposed node has been implemented with two radios, with different roles. A GPRS modem has been exclusively implemented for image transmission, while all other tasks, including node monitoring and camera control, are performed by a LoRaWAN class A end-node that connects every 10 min. Via the LoRaWAN downlink, it is possible to efficiently control the camera settings; the shooting times and periodicity, according to weather conditions; the eventual farming operations; the crop growth stages and the season. The node energy consumption has been verified in the laboratory and in the field, showing that it is possible to acquire one picture per day for more than eight months without any energy harvester, opening up further possible implementations for disease detection and production optimization.

An engine to simulate insurance fraud network data

An engine to simulate insurance fraud network data

Signal Reconstruction Based on Time‐Varying Sliding Window in WSNs Using Compressed Sensing

ABSTRACTThis paper presents a new algorithm that utilizes compressed sensing (CS) for reconstruction of wireless sensor networks (WSNs) data with spatial and temporal correlation. The proposed method utilizes a time‐varying sliding window mechanism that dynamically adjusts both the window size and the number of measurements. This flexibility allows the algorithm to exploit spatio‐temporal correlations effectively, ensuring that data within the window remains sparse and thus more compressible. By dynamically varying the number of measurements, the algorithm equitably distributes the sampling rate across different time slots, adapting to changes in signal characteristics and minimizing transmission costs. Simulation results demonstrate that our proposed algorithm outperforms other CS reconstruction methods by achieving higher reconstruction precision while requiring fewer transmissions. This is achieved through a decentralized data‐window framework that maximizes the use of prior signal information, leading to improved signal recovery performance in diverse WSN scenarios.

Dynamic SelectOut and voting-based federated learning for enhanced medical image analysis

Abstract Federated Learning (FL) is a promising technique for training machine learning models on distributed, privacy-aware datasets. Nevertheless, FL faces difficulties with agent/client participation, model performance, and the heterogeneous nature of networked data sources when it comes to distributed healthcare systems. When these agents work together in the system, it is imperative to tackle the complexities of distributed deep learning. We suggest a novel approach that uses a voting mechanism and dynamic SelectOut inside the FL framework to address these problems. Local medical imaging datasets frequently show diversity in distribution and data imbalances. In certain situations, traditional FL techniques like FedProx and Federated Averaging (FedAvg), which depend on data size to weight contributions, might not be the optimal choice. We improve parameter aggregation and client selection unpredictability and increase the model's adaptability to imbalanced and heterogeneous datasets, our proposed FedVoteNet model introduces SelectOut techniques based on Voting methodology. Based on how much their local performance has improved from the last communication cycle, we arbitrarily remove clients. Additionally eliminated are clients whose model weights when combined with the global model adversely affect its performance. After each communication cycle, clients that improve both their local performance and their contribution to the global model are awarded higher voting values. This encourages more significant and effective contributions from clients by providing incentives for them to actively increase the diversity of their training data. We assess our approach on a dataset of medical images, including MRI scans, and find that the FL model performs noticeably better (F1 Score=0.968, Sensitivity=0.977, Specificity=0.945, and AUC=0.950). The voting system and the dynamic SelectOut algorithms improve the convergence of the FL model and successfully handle the difficulties presented by uneven and heterogeneous datasets. To sum up, our proposed approach uses voting and dynamic SelectOut techniques to improve FL performance on a variety of uneven, distributed, and varied datasets. This strategy has a lot of potential to improve FL across a range of applications, especially those that prioritize data privacy, diversity, and performance.

The Potential Impact of the European Green Deal on Farm Production in Poland

The proposed changes in the European Green Deal require the restructuring of the agricultural sector throughout the European Union. Particularly crucial for the agri-food industry are “From Farm to Fork” strategies and new legislation “for biodiversity,” which are an integral part of the European Green Deal. From an environmental perspective, changes are required immediately, but at the same time, they may reduce agricultural production in Poland. Therefore, this study aims to assess the potential consequences of implementing the European Green Deal principles on Polish agriculture’s productivity. The study is based on two key assumptions which involve a 50% reduction in the use of plant protection products and a 20% reduction in fertilisation. The conducted analyses rely on data from the Farm Accountancy Data Network (FADN). The results were obtained by constructing a panel regression model for farms of different economic sizes. Although the most significant reduction in production value may concern the largest farms (using the most fertilisers and plant protection products), the smallest farms may experience the most critical difficulties. The potential decline in crop production in Polish agriculture could be stopped by implementing modern technologies enabling the development of precision and digital agriculture 4.0.

Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers

Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations using quantities measured at the wall. In Guastoni et al. (J Fluid Mech 928:A27, 2021. https://doi.org/10.1017/jfm.2021.812), we focused on wall-shear-stress distributions as input, which are difficult to measure in experiments. In order to overcome this limitation, we introduce a model that can take as input the heat-flux field at the wall from a passive scalar. Four different Prandtl numbers Pr=ν/α=(1,2,4,6) are considered (where ν is the kinematic viscosity and α is the thermal diffusivity of the scalar quantity). A turbulent boundary layer is simulated since accurate heat-flux measurements can be performed in experimental settings: first we train the network on aptly-modified DNS data and then we fine-tune it on the experimental data. Finally, we test our network on experimental data sampled in a water tunnel. These predictions represent the first application of transfer learning on experimental data of neural networks trained on simulations. This paves the way for the implementation of a non-intrusive sensing approach for the flow in practical applications.

Analysis of Variance of Tensor Product Reproducing Kernel Hilbert Spaces on Metric Spaces

Many methods have been developed to analyze complex data, such as non-Euclidean shape, network, and manifold data. However, there is a lack of methods for studying interactions among complex data. In this paper, we first propose a novel kernel function for a metric space and construct its associated reproducing kernel Hilbert space. The new nonstationary kernel function provides a flexible and powerful tool for learning complex structures in non-Euclidean data. We then construct an analysis of variance (ANOVA) decomposition of the nonparametric regression function defined on metric space, which provides a hierarchical structure for investigating the main effects and interactions. We develop estimation and computational methods for a semi-parametric model with a multivariate function on a product of metric spaces modeled by the ANOVA decomposition. We establish the convergence rates of parameter and nonparametric function estimates. The application of the proposed methods to the Alzheimer’s Disease Neuroimaging Initiative hippocampus shape data confirms some existing and suggests some new interactions among hippocampal regions. Simulations indicate that the proposed methods work well.

Overview of Hieroglyph in Teaching Chinese as a Foreign Language

In teaching Chinese as a foreign language, Chinese characters teaching is an essential part of teaching. With the continuous development of teaching Chinese characters, the Hieroglyph teaching plays a positive role in the process of helping foreign students learn Chinese characters. By retrieving documents in China Knowledge Network and Wanfang Data Knowledge Service Platform, this paper reviews the status of the Hieroglyph teaching in teaching Chinese as a foreign language. Based on the basic situation of the Hieroglyph teaching, this paper analyzes the historical development and advantages of it, points out its guide ideas, and summarizes relevant research of teaching methods. Through the comparison of the Hieroglyph teaching methodology and traditional Chinese characters teaching methodology, this paper further explores the significance of the Hieroglyph teaching, finally, the shortcomings of it and suggestions for improvement are pointed out, aims to demonstrate the feasibility and practical significance of the Hieroglyph teaching. This paper summarizes the current basic situation, potential problems and suggestions for improvement of this research field in the Hieroglyph teaching, which can help experts and scholars enhance the depth and breadth in future.

Selection of Grid Road Networks Based on Raster Data

In cartography, generalization is a key process used to simplify complex geographic information, making it suitable for display at different scales while maintaining its essential meaning. When representing high-density road networks on a fixed screen area, overcrowding and loss of clarity often occur. To solve these problems, a road selection operation can be applied. However, traditional methods have primarily focused on structured vector road networks, leaving unstructured raster road networks largely unaddressed. This study introduces a novel technique, Adaptive Road Width Selection (ARWS), designed to improve the multiscale visualization of compact road systems using unstructured raster datasets. The ARWS method begins by segmenting the original raster road network into multilevel superpixels of varying sizes, reflecting the road widths, through neighborhood analysis. Next, road superpixel matching and selection are performed based on the minimum angle and maximum distance rules, alongside shortest-path calculations. Finally, redundant intersection pixels are eliminated to generate the selection results. The proposed ARWS method was evaluated using road network data from Shenzhen, China, producing effective multiscale visualization outcomes. Unlike conventional techniques relying on structured vector data, ARWS excels in preserving the semantic attributes, overall structure, local connectivity, and integrity of road networks. It addresses the challenges of multiscale visualization in dense road networks, offering a robust solution for unstructured raster data.

Assessing cross-docking performance using a novel network DEA model regarding sustainability and undesirable factors under uncertainty

Although evaluating warehouses' efficiency using different approaches has been gaining prestige in recent years, this study is the first attempt to assess the efficiency of cross-docking systems. For this purpose, a comprehensive structure of a cross-docking system is examined, where a broad range of components affecting its performance are considered, including inbound and outbound doors, various means of transportation, inspection, kitting, storage, retrieval, and staging operations. In addition, an extensive set of key performance indicators (KPIs) are introduced for each component regarding automation, digitization, resiliency, sustainability, and lean concerns. More importantly, a novel network data envelopment analysis model is proposed to evaluate the efficiency of the cross-docking system with respect to undesirable factors. Furthermore, a new hybrid uncertainty approach is offered to handle the uncertainties in introduced KPIs and quantify some qualitative judgments. Finally, a case study is examined to illustrate the effectiveness of the proposed model and uncertainty approach. The obtained results reveal that the simultaneous exploitation of pre-distribution and post-distribution policies can increase the efficiency of the examined system by 36%. Also, modifying the layout to eliminate redundant transportation operations can lead to an 18% increase in efficiency.

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.

Disease-modifying treatment and disability progression in subclasses of patients with primary progressive MS: results from the Big MS Data Network

BackgroundEffectiveness of disease-modifying treatment (DMT) in people affected by primary progressive multiple sclerosis (PPMS) is limited. Whether specific subgroups may benefit more from DMT in a real-world setting remains unclear....

ALAAMEE: Open-source software for fitting autologistic actor attribute models

The autologistic actor attribute model (ALAAM) is a model for social influence, derived from the more widely known exponential-family random graph model (ERGM). ALAAMs can be used to estimate parameters corresponding to multiple forms of social contagion associated with network structure and actor covariates. This work introduces ALAAMEE, open-source Python software for estimation, simulation, and goodness-of-fit testing for ALAAM models. ALAAMEE implements both the stochastic approximation and equilibrium expectation (EE) algorithms for ALAAM parameter estimation, including estimation from snowball sampled network data. It implements data structures and statistics for undirected, directed, and bipartite networks. We use a simulation study to assess the accuracy of the EE algorithm for ALAAM parameter estimation and statistical inference, and demonstrate the use of ALAAMEE with empirical examples using both small (fewer than 100 nodes) and large (more than 10 000 nodes) networks.

Hospital system market share and commercial prices: a cross-sectional approach using price transparency data

BackgroundThe goal of this study is to estimate the association between hospital system market share and negotiated prices.Hospital system consolidation has led to many highly concentrated markets where systems can leverage their market share to negotiate higher commercial prices. Recently, the Centers for Medicare & Medicaid Services, under its Transparency in Coverage initiative, required health insurers to release all negotiated commercial prices, providing, for the first time, publicly available, nationally representative data on commercial rates. We utilize this newly available data on negotiated prices of healthcare services to show that a hospital with 10% higher market share charges 880-1,180 more per admission.Study designWe used commercial price data for national networks of three large, national insurers and performed a linear regression based on more than 1.3 million negotiated rates across 1,784 hospitals to estimate the association between a hospital’s system-level market share and commercial negotiated rates, adjusting for service (DRG), health system, and area level time-invariant characteristics.ResultsWe find that a one percentage point increase in hospital system market share is associated with an $88 to $118 higher negotiated rate per admission. All else equal, a hospital that is part of a system with a 10-percentage point higher market share can expect from $880 to $1,180 more per admission relative to a hospital with lower system market share (5.4% to 6.2% of the median price).ConclusionThese findings confirm that higher hospital system market share is strongly associated with higher commercial negotiated prices and should aid policymakers and decisionmakers in assessing the impact of various policy options aimed at reducing provider consolidation in the healthcare market.Trial registrationNot applicable.

Collaboration strategies for bridging health, behavioral health, and social services in California's Medi-Cal Whole Person Care Pilot Program.

To identify collaboration strategies used to integrate health, behavioral health, and social services for Medicaid members in California's Medi-Cal Whole Person Care Pilot program (WPC). WPC was a social care intervention implemented to identify and address eligible members' health, behavioral health, and social needs. Data included semi-structured key informant interviews conducted in 2018-2019 (n = 221) and 2021 (n = 167); pilot-level surveys; whole-network surveys of 507 organizations in all 25 pilots participating in WPC; and documents submitted by pilots to the state. Pilots served a total of 247,887 unique members between 2017 and 2021, the majority of whom were non-white (72%) and over half of whom experienced homelessness. Data were collected as part of the statewide evaluation of WPC. We analyzed qualitative data to examine strategies used by pilots to integrate care, network data to identify pilots that improved cross-sector collaboration (i.e., strengthened density or multiplexity of cross-sector ties) following WPC implementation, and comparative case analysis to identify strategies that differentiated pilots that improved collaboration from those that did not. Pilots used multiple strategies to facilitate the integration of care. Network analyses identified 10 pilots that significantly improved either density or multiplexity of cross-sector ties, and one pilot with high cross-sector collaboration prior to WPC. Compared to pilots that did not improve cross-sector collaboration, these pilots meaningfully engaged partners in program design and implementation, used braided funds, and leveraged WPC to support broader systems change. These pilots also reported fewer challenges in developing and managing contractual relationships and ensuring meaningful use of data-sharing infrastructure by frontline staff responsible for care coordination. Data sharing is necessary but not sufficient for systems alignment. Collaboration strategies focused on addressing financial barriers to integration and strengthening normative and interpersonal integration are also needed.

EGNAS: Efficient Graph Neural Architecture Search Through Evolutionary Algorithm

The primary objective of our research is to enhance the efficiency and effectiveness of Neural Architecture Search (NAS) with regard to Graph Neural Networks (GNNs). GNNs have emerged as powerful tools for learning from unstructured network data, compensating for several known limitations of Convolutional Neural Networks (CNNs). However, the automatic search for optimal GNN architectures has seen little progressive advancement so far. To address this gap, we introduce the Efficient Graph Neural Architecture Search (EGNAS), a method that leverages the advantages of evolutionary search strategies. EGNAS incorporates inherited parameter sharing, allowing offspring to inherit parameters from their parents, and utilizes half epochs to improve optimization stability. In addition, EGNAS employs a combined evolutionary search, which explores both the model structure and the hyperparameters within a large search space, resulting in improved performance. Our experimental results demonstrate that EGNAS outperforms state-of-the-art methods in node classification tasks on the Cora, Citeseer, and PubMed datasets while maintaining a high degree of computational efficiency. In particular, EGNAS is the fastest GNN architecture search method in terms of search time, particularly when compared to precedently suggested evolutionary search strategies, delivering performance up to 40 times faster.