Practical Network Research Articles

Making It Magical

Making It Magical

UK advanced practice educator networks

Over the past three decades, several advance practice networks have developed across the UK. This article examines three of the largest, from their inception to contemporary forms, and highlights the differences between each.

Mutual Sustainability among Communities and Their Knowledge Infrastructures

ABSTRACTDigital, community‐based knowledge infrastructures confront complex, systemic challenges to their sustainability over time. From digital community archives to computationally amenable corpora, databases, or data models created by and serving research communities, these sites of grassroots knowledge production tend to be maintained without long‐term institutional commitments. Yet, they hold unique cultural evidence of enormous value. Prior work on the sustainability of digital humanities scholarship has surfaced numerous factors in project sustainability, including technical, organizational, and financial concerns. The roles of communities themselves in sustaining community‐based knowledge infrastructures, however, are under‐studied. This qualitative, multi‐case study of digital humanities projects and digital community archives addresses community‐centered approaches to understanding and implementing sustainability for digital knowledge infrastructures. This study finds that communities of various kinds—from public communities to networks of research practice—conceive of the sustainability of their digital projects as inextricably linked to the sustainability of communities themselves. We offer an exploration of factors in the mutual relationship of sustainability between communities and their knowledge infrastructures, including how they support the wellbeing of individuals and cohesion among communities, and how they promote activism and advocacy efforts within broader publics and disciplinary cultures.

Real-World Toxicity of Conventional Versus Hypofractionated Definitive Prostate Radiotherapy across a Large, Diverse, Academic and Community-Based Enterprise.

Over the past 10-15 years there has been increasing adoption of moderate hypofractionation (HF) for definitive prostate radiotherapy as compared to conventional fractionation (CF). Based on several randomized trials hypofractionation results in equivalent treatment efficacy with similar rates of long-term toxicity. However, some studies suggest higher acute GI toxicity with moderate hypofractionation. We sought to compare the rates of toxicity between these two groups across our enterprise including 16 community-based practices and one academic NCI-designated comprehensive cancer center. We retrospectively extracted radiation treatment intent from our network-wide clinical pathways for patients diagnosed with prostate cancer between 3/2019 and 10/2022. Patients treated after prostatectomy and those treated with brachytherapy or SBRT were excluded. For the remaining 1529 patients treated with either conventional fractionation or moderate hypofractionation, we identified and merged physician-graded toxicity data using CTCAE version 5.0 recorded in their electronic medical record at each weekly on-treatment visit and follow-up. A total of 1051 patients had toxicity data available. Rates of toxicities were then compared between the cohort of patients who received CF and those who HF using the Chi-square test. Of the 1051 patients, 450 (43%) received CF and 601 (57%) received HF. These patients were treated by 40 different radiation oncologists (median patients per physician = 18, interquartile range = 7-35). Median age in the CF and HF cohorts was 71 (IQR: 66-76) and 71 (IQR: 66-77; p = 0.51), respectively. The CF cohort had more patients with Gleason 8+ disease (39% vs 19%; p<0.01), PSA >20 (26% vs 11%; p<0.01), or T3a+ (18% vs 8%; p<0.01). Rates of any grade 2+ toxicity were significantly higher in patients who received HF at 45.8% vs 39.6% for those treated with CF (p = 0.04). However, the respective rates of any grade 3+ toxicity were no different at 2.0% vs. 1.8% (p = 0.80). The difference in grade 2 toxicities appeared to be primarily driven by the rates of urinary frequency at 27.1% vs. 17.8% (p<0.01) and prostatic obstruction 14.8% vs. 10.2%, p = 0.03). Rates of grade 2 diarrhea were worse with MF at 5.3% vs. 2.8% for CF (p = 0.04). There were no significant differences between HF and CF in the rates of grade 2 dysuria (6% vs 5.2%), urinary urgency (6.5% vs. 4.2%), proctitis (3.0% vs. 3.6%), urinary incontinence (0.5% vs. 1.3%), rectal bleeding (0.3% vs. 0%), hematuria (0% vs. 0.4%), and fatigue (14.1% vs. 15.1%). In this large network-wide analysis, toxicity was slightly increased among patients with prostate cancer treated with HF compared to CF, consistent with published randomized data. However, the increased toxicity appeared to be primarily GU rather than GI. This study demonstrates the feasibility of analyzing impacts of treatment decisions on a large scale using real-world data through an integrated network of practices.

Automated detection of multi-class urinary sediment particles: An accurate deep learning approach

Urine microscopy is an essential diagnostic tool for kidney and urinary tract diseases, with automated analysis of urinary sediment particles improving diagnostic efficiency. However, some urinary sediment particles remain challenging to identify due to individual variations, blurred boundaries, and unbalanced samples. This research aims to mitigate the adverse effects of urine sediment particles while improving multi-class detection performance. We proposed an innovative model based on improved YOLOX for detecting urine sediment particles (YUS-Net). The combination of urine sediment data augmentation and overall pre-trained weights enhances model optimization potential. Furthermore, we incorporate the attention module into the critical feature transfer path and employ a novel loss function, Varifocal loss, to facilitate the extraction of discriminative features, which assists in the identification of densely distributed small objects. Based on the USE dataset, YUS-Net achieves the mean Average Precision (mAP) of 96.07%, 99.35% average precision, and 96.77% average recall, with a latency of 26.13 ms per image. The specific metrics for each category are as follows: cast: 99.66% AP; cryst: 100% AP; epith: 92.31% AP; epithn: 100% AP; eryth: 92.31% AP; leuko: 99.90% AP; mycete: 99.96% AP. With a practical network structure, YUS-Net achieved efficient, accurate, end-to-end urinary sediment particle detection. The model takes native high-resolution images as input without additional steps. Finally, a data augmentation strategy appropriate for the urinary microscopic image domain is established, which provides a novel approach for applying other methods in urine microscopic images.

Rapid Reinstatement of Cancer Care after Hurricane Ian

Category 4 Hurricane Ian brought significant destruction and challenges to western Florida's healthcare facilities. A Radiation Oncology practice network serving the area minimized interruptions in care by implementing planned and improvised solutions. Examining actions that allowed rapid recovery of care could assist others in disaster response. The National Hurricane Center issued a hurricane warning 36 hours prior to Ian's landfall. Center closures were based on evacuations and scheduled take down of the power grid. Computers were unplugged, elevated and wrapped in plastic. The LINAC was isolated from the power grid at the main breaker. Staff and patient contacts were printed. Communication techniques were agreed upon for post-storm scenarios with or without cellular service and internet. Hurricane Ian made landfall on September 28 with 150 mph sustained winds and gusts of 216 mph. Unsafe conditions prevented action on September 28. Facility assessment began on September 29. The majority of facilities were inaccessible due to impassible roads and entry hazards, or could not be safely occupied. Based on location and condition, one center from the network of facilities was selected for immediate operational restoration. After confirmation that the site's restored power would remain stable, the LINAC was powered on. An onsite engineering evaluation was done to assess machine functionality and vacuum integrity. Medical Physics then performed monthly QA and isocenter verification due to concern for building vibrations from extreme winds. Interfacing with patient charts in the Aria database was more complex than anticipated. Aria is a hub that uses relative paths to refer to plans, structure sets, images, and radiotherapy treatment records stored in remote servers. These Aria servers could not be accessed because the remote sites had no power. As the Aria servers could not be moved, teams went to retrieve the related Adaptivo servers to allow re-import of patient plans. When booted these servers had no common network and no graphical user interface. Using command line tools, the servers were added to the hub network and IP addresses were assigned. MS-DOS command Robocopy allowed a large-scale resumable transfer from Adaptivo patient folders to the Eclipse import directory. Plan validation and QA followed. Records could not be updated with delivered treatments, necessitating reconciliation of total dose when normal operations resumed. Within 72 hours of Ian passing, treatment resumed for all patients within the cancer center network who were able to access the restored site. Within days, seven of the eight regional centers were operational and patients were redistributed. The unexpected challenges posed by data and network access suggest that improving data location and back-up networks could facilitate center response and resiliency in future disasters.

Scheduling in Wireless Cooperative Localization Networks with Intelligent Eavesdroppers

Location-based service based on wireless cooperative localization networks is becoming ubiquitous nowadays. However, since the fact that most location network nodes are resource-limited, recent investigations focus on the proper network scheduling strategies that can significantly enhance the system performance, including but not limited to localization accuracy and energy efficiency. In addition to the current efficient nondata-aided strategies, we find that some silent nodes, called “eavesdroppers,” can be helpful to the localization task without transmitting any signals. In this paper, we first formulate the eavesdropping scheduling policy in practical asynchronous cooperative wireless localization networks. Then, we perform resource optimization in different eavesdropping-based strategies. Both two-slot and multislot strategies are considered, and three types of listening modes are designed from a practical point of view. Numeric results show that, for the scenario with a blocked propagation path, the localization error of networks with dedicated eavesdroppers is only 21% of the conventional networks. Besides, the system with eavesdropping anchors could improve the localization performance by 70%. The result could provide meaningful insights into the practical low-complexity location network deployment and development.

Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications

With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting.

Arrhythmia classification detection based on multiple electrocardiograms databases.

According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.

Towards metropolitan free-space quantum networks

Quantum communication has seen rapid progress towards practical large-scale networks, with quantum key distribution (QKD) spearheading this development. While fibre-based systems have been shown to be well suited for metropolitan scales, suitable fibre infrastructure may not always be in place. Here, we make the case for an entanglement-based free-space quantum network as a practical and efficient alternative for metropolitan applications. We developed a deployable free-space QKD system and demonstrated its use in realistic scenarios. For a representative 1.7-km free-space link, we showcase its ad hoc deployability and achieve secure key rates of up to 5.7 kbps, with 2.5 kbps in direct noon sunlight. By extrapolating experimental data, we show that kbps key rates are achievable even for 10-km distances and multi-user scenarios. We anticipate that our work will establish free-space networks as a viable solution for metropolitan applications and an indispensable complementary building block in the future global quantum internet.

The loci of Pluralism in a war-torn society

ABSTRACT The war in Ukraine is violently destroying the social, political, cultural and economic fabrics of society and questions regarding the resilience of ethnicity and pluralism is becoming increasingly relevant. The existing research frameworks on pluralism consider pluralism as an integrative concept based on the different social practices and governance institutions for diversity and the realization of the maximum potential of all individuals in society (S. Wolff). In order to deeply understand the issue, we structured our research around next questions: how social practices of pluralism are affected by war? In which way do the country’s governmental institutions, facing military aggression, respond to pluralistic values? And, finally, how Ukrainian war-triggered migration transfer existing practices of pluralism but also adopted practices in new locations? Relying on the present scholarship of pluralism during the crises, adapting data of national surveys and incorporating the most recent data from the agencies, tracing the democratic development and pluralism, this research aims to trace national institutions and transnational networks of pluralistic practices during the war.

Anonymous conference key agreement in linear quantum networks

Sharing multi-partite quantum entanglement between parties allows for diverse secure communication tasks to be performed. Among them, conference key agreement (CKA) - an extension of key distribution to multiple parties - has received much attention recently. Interestingly, CKA can also be performed in a way that protects the identities of the participating parties, therefore providing anonymity. In this work, we propose an anonymous CKA protocol for three parties that is implemented in a highly practical network setting. Specifically, a line of quantum repeater nodes is used to build a linear cluster state among all nodes, which is then used to anonymously establish a secret key between any three of them. The nodes need only share maximally entangled pairs with their neighbours, therefore avoiding the necessity of a central server sharing entangled states. This linear chain setup makes our protocol an excellent candidate for implementation in future quantum networks. We explicitly prove that our protocol protects the identities of the participants from one another and perform an analysis of the key rate in the finite regime, contributing to the quest of identifying feasible quantum communication tasks for network architectures beyond point-to-point.

Influence of research evidence on the use of cardiovascular clinical prediction rules in primary care: an exploratory qualitative interview study

BackgroundCardiovascular clinical prediction rules (CPRs) are widely used in primary care. They accumulate research evidence through derivation, external validation, and impact studies. However, existing knowledge about the influence of research evidence on the use of CPRs is limited. Therefore, we explored how primary care clinicians’ perceptions of and experiences with research influence their use of cardiovascular CPRs.MethodsWe conducted an exploratory qualitative interview study with thematic analysis. Primary care clinicians were recruited from the WWAMI (Washington, Wyoming, Alaska, Montana and Idaho) region Practice and Research Network (WPRN). We used purposeful sampling to ensure maximum variation within the participant group. Data were collected by conducting semi-structured online interviews. We analyzed data using inductive thematic analysis to identify commonalities and differences within themes.ResultsOf 29 primary care clinicians who completed the questionnaire, 15 participated in the interview. We identified two main themes relating to the influence of clinicians’ perceptions of and experiences with cardiovascular CPR research on their decisions about using cardiovascular CPRs: “Seek and judge” and “be acquainted and assume.” When clinicians are familiar with, trust, and feel confident in using research evidence, they might actively search and assess the evidence, which may then influence their decisions about using cardiovascular CPRs. However, clinicians, who are unfamiliar with, distrust, or find it challenging to use research evidence, might be passively acquainted with evidence but do not make their own judgment on the trustworthiness of such evidence. Therefore, these clinicians might not rely on research evidence when making decisions about using cardiovascular CPRs.ConclusionsClinicians’ perceptions and experiences could influence how they use research evidence in decisions about using cardiovascular CPRs. This implies, when promoting evidence-based decisions, it might be useful to target clinicians’ unfamiliarity, distrust, and challenges regarding the use of research evidence rather than focusing only on their knowledge and skills. Further, because clinicians often rely on evidence-unrelated factors, guideline developers and policymakers should recommend cardiovascular CPRs supported by high-quality evidence.

Diagnostic properties of natriuretic peptides and opportunities for personalized thresholds for detecting heart failure in primary care.

Heart failure (HF) is a prevalent syndrome with considerable disease burden, healthcare utilization and costs. Timely diagnosis is essential to improve outcomes. This study aimed to compare the diagnostic performance of B-type natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP) in detecting HF in primary care. Our second aim was to explore if personalized thresholds (using age, sex, or other readily available parameters) would further improve diagnostic accuracy over universal thresholds. A retrospective study was performed among patients without prior HF who underwent natriuretic peptide (NP) testing in the Amsterdam General Practice Network between January 2011 and December 2021. HF incidence was based on registration out to 90 days after NP testing. Diagnostic accuracy was evaluated with AUROC, sensitivity and specificity based on guideline-recommended thresholds (125 ng/L for NT-proBNP and 35 ng/L for BNP). We used inverse probability of treatment weighting to adjust for confounding. A total of 15,234 patients underwent NP testing, 6,870 with BNP (4.5 % had HF), and 8,364 with NT-proBNP (5.7 % had HF). NT-proBNP was more accurate than BNP, with an AUROC of 89.9 % (95 % CI: 88.4-91.2) vs. 85.9 % (95 % CI 83.5-88.2), with higher sensitivity (95.3 vs. 89.7 %) and specificity (59.1 vs. 58.0 %). Differentiating NP cut-off by clinical variables modestly improved diagnostic accuracy for BNP and NT-proBNP compared with a universal threshold. NT-proBNP outperforms BNP for detecting HF in primary care. Personalized instead of universal diagnostic thresholds led to modest improvement.

The security policy community and the consensus on the US–Japan alliance: the role of think tanks, experts and the alliance managers

The article addresses the issue of continuity of the consensus on the US–Japan alliance in the post-Cold War era, by employing the concepts of a security policy community (SPC) that comprises both government office-holders and non-governmental actors focused on the foreign and security policy. The main argument is three-fold; firstly, one of the major forces behind the continuity and enhancement of the security consensus on the US–Japan alliance has been the formation of the transnational SPC that includes members from the US and Japan; secondly, since the end of the Cold War, the SPC has developed a dense network of members, institutions, and practices that supports the advocacy of alliance enhancement as well as community cohesion and nurturing of new members; and thirdly, the think tank- and university-based experts play an increasingly vital role in the formation and dissemination of the security consensus. The article suggests that such arrangements can have both positive and negative consequences and that a proper balance between openness and exclusiveness, expertise and novelty, and continuity and innovation is required in order to maintain a vibrant marketplace of ideas for security policy. The article also indicates that with the transformation of the Indo-Pacific security architecture from the hub and spoke to the networked system, SPC might serve as a model in other US alignments.

Data Perspectives in AI-Assisted Fiber-Optic Communication Networks

Machine learning (ML) is widely being regarded as a powerful technology to solve various complex problems in fiberoptic communication networks. Recently, ML algorithms have been considered for a variety of use-cases in optical networks ranging from the design of network devices to the compensation of transmission impairments to predicting network-wide traffic flow patterns. As ML techniques are inherently data driven, the availability of relevant data sets largely dictates the performance of developed ML models. Presently, majority of ML-aided methods in the literature rely either on synthetic data from certain simulation tools or exploit data from some simple laboratory setups. However, developing robust ML-based solutions for real fiber-optic networks demands assembling big data from various parts as well as across different layers and domains of the network. Unfortunately, there is currently a lack of understanding among the research community about various crucial data aspects in practical optical networks as well as key challenges faced. While there has been a plethora of works discussing numerous ML applications in fiber-optic communications and networking, to the best of our knowledge, there is presently no paper which particularly focuses on data perspectives. In this paper, we aim to shed light on various critical data factors by first describing the tools and technologies imperative for adequate network monitoring and data acquisition. We also elucidate different data types, their physical sources, and their applications. Several useful data handling and pre-processing techniques for efficient network data collection, storage, presentation, analysis, etc., are discussed. Major challenges in the generation, transmission, and sharing of the data are highlighted and some potential solutions to the outstanding problems are proposed, thus paving the way for seamless availability of pertinent data sets essential for realizing intelligent data-driven operations in future fiber-optic networks.

Optimal placement of battery energy storage systems with energy time shift strategy in power networks with high penetration of photovoltaic plants

This paper introduces a novel approach for the optimal placement of battery energy storage systems (BESS) in power networks with high penetration of photovoltaic (PV) plants. Initially, a fit-for-purpose steady-state, power flow BESS model with energy time shift strategy is formulated following fundamental operation principles. The optimal BESS placement methodology is subsequently developed in the realm of incremental modelling of power system losses, which permits to identify the best candidate node for the BESS connection that reduces the total power losses. And for practical results, daily varying patterns of nodal demand, generation dispatch and solar irradiance are considered, all this accompanied by the BESS operating strategy, i.e., electric energy time shift, which is one of the most sounding strategic applications in current BESS facilities. In addition to providing a suitable validation proof using the standard IEEE 5-bus test system, two practical test power network models with 24 and 118 nodes are used to showcase the usefulness of the incremental modelling approach for optimal BESS placement in power grids with high penetration of PV plants.

Integrated sensing and communication based outdoor multi-target detection, tracking, and localization in practical 5G Networks

Integrated sensing and communication based outdoor multi-target detection, tracking, and localization in practical 5G Networks

Security Fuzzy Control of Nonlinear Semi-Markov Switching Systems With Event-Based Mechanism and Hybrid Cyber-Attacks

This article is concerned with the security <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> fuzzy control problem for Takagi–Sugeno (T–S) fuzzy semi-Markov switching systems (SMSSs) with event-based strategy and hybrid cyber-attacks. To efficiently use network resources and avoid unnecessary signal transmission, a new mode-dependent dynamic event-triggered mechanism (ETM), whose threshold parameter changes with varying states and modes is developed for each subsystem of fuzzy SMSSs. A more practical network environment subject to deception and denial-of-service (DoS) attacks is considered, and two Bernoulli distributed variables are adopted to characterize the randomly occurring hybrid attacks in a unified framework. By the mode-dependent Lyapunov function, sufficient conditions are attained to assure that the closed-loop system is stochastically stable with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> attenuation performance. Finally, the validity and superiority of the proposed theoretical approach is demonstrated via its application to the Duffing–Van der Pols oscillator.

Efficient Network Slicing for 5G Services in Cloud Fog-RAN Deployment Over WDM Network

Network slicing in fifth generation (5G) radio access network (RAN) enables serving massive network traffic with diverse and stringent quality of service (QoS) requirements. Multiple logical networks can be built using RAN slicing over a single RAN infrastructure. This paper considers three different types of slices that are standardized by third generation partner project (3GPP): ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine type communication (mMTC). Each slice type has unique requirements for data rate, latency, and reliability. Cloud RAN (C-RAN) was proposed to address the requirements of 5G services, which involves physical separation between the remote radio head (RRH) and baseband unit (BBU) in 2-layers. While C-RAN enables more efficient resource utilization and energy consumption, it limits network scalability. Furthermore, C-RAN is unable to meet the stringent latency demands of URLLC services, as well as the massive fronthaul capacity required for eMBB requests. To address this issue, we propose a novel cloud fog RAN (CF-RAN) over wavelength division multiplexing (WDM) architecture. In CF-RAN over WDM the RAN functions are divided into 3 layers, which include the RRH at layer 1, fog nodes at layer 2, and BBU hotels at layer 3. We employ the emerging fog computing paradigm in optical and wireless networks in this suggested architecture. To facilitate low latency URLLC requests, the fog nodes are located closer to the cell site (CS). We propose an integer linear programming (ILP)-based mathematical model. The model's objective is to decrease the number of active BBU hotels and fog nodes while remaining compliant with practical network restrictions. Furthermore, a low complexity greedy heuristic algorithm is proposed to solve the problem and is compared to the branch & bound (B&B) algorithm, which is assumed to provide an optimal solution with exponential complexity. The proposed 3-layer CF-RAN over WDM architecture achieves a 70% improvement in BBU centralization and a 50% reduction in request blocking when compared to the standard 2-layer architecture.