Practical Network Research Articles

Characterization of the real-world first line (1L) use of single dose tremelimumab regular interval durvalumab (STRIDE), for treatment of unresectable hepatocellular carcinoma (uHCC), using two electronic medical record (EMR) databases.

534 Background: In October 2022, the US FDA approved the STRIDE (Single Dose Tremelimumab Regular Interval Durvalumab) regimen, comprising tremelimumab 300 mg for 1 dose plus durvalumab 1500 mg every 4 weeks, for the 1L treatment of uHCC in adults aged ≥18 years. Thus far there is limited data characterizing STRIDE's 1L use in the real world. This observational study aims to describe patient characteristics, and clinical profiles of a 1L STRIDE cohort. Methods: A retrospective cohort study was conducted using EMR data from two community oncology practice networks, Florida Cancer Specialists and American Oncology Network, from January 1, 2018, to March 31, 2024. Adults with HCC who received ≥1 administration of the STRIDE regimen as 1L treatment following diagnosis were included. Patients with evidence of other cancers or systemic therapies pre-index date were excluded, to establish a cancer-naïve cohort at index. Index date was first administration of STRIDE. The analysis described the patient characteristics and clinical profile at index date. Patient demographics, severity indicators (cancer stage, albumin-bilirubin index (ALBI), alpha fetoprotein (AFP), Eastern Cooperative Oncology Group (ECOG)) and lab data were assessed on or closest to index date. Results: 79 patients initiated the 1L STRIDE regimen. The median (P25, P75) age at index was 70 (63.2-76.4) years. 72.2% were males, 64.6% were white, and 69.6% were overweight or obese patients. 95% had their first HCC diagnosis between 2022-2024 and the mean time from diagnosis to index date was 3.8 months (SD: 10.2). Among patients with reported cancer stage (n=57), most (82.5%, n=47) were diagnosed with stages IIIA to IVB. 18% had ECOG scores ≥2. 86% had ALBI grades between 2-3; 42.1% had AFP level ≥400 ng/ml; 30.4% had cirrhosis (among those that reported comorbidities) and 24.1% received any loco-regional therapy. Conclusions: Our study provides a real-world profile of patients on 1L STRIDE. Compared to the HIMALAYA trial, these findings suggest that patients in this real-world study tend to be older, diagnosed at late-stage, have a poor liver profile, and potentially worse prognosis over time. Further research is required to explore treatment pathways after index date, over a long follow-up, which may provide more insight into the continuity and effectiveness of IL STRIDE in the real-world.

“Everybody knows what a pothole is”: representations of work and intelligence in AI practice and governance

Abstract In this paper, we empirically and conceptually examine how distributed human–machine networks of labour comprise a form of underlying intelligence within Artificial Intelligence (AI), considering the implications of this for Responsible Artificial Intelligence (R-AI) innovation. R-AI aims to guide AI research, development and deployment in line with certain normative principles, for example fairness, privacy, and explainability; notions implicitly shaped by comparisons of AI with individualised notions of human intelligence. However, as critical scholarship on AI demonstrates, this is a limited framing of the nature of intelligence, both of humans and AI. Furthermore, it dismisses the skills and labour central to developing AI systems, involving a distributed network of human-directed practices and reasoning. We argue that inequities in the agency and recognition of different types of practitioners across these networks of AI development have implications beyond RAI, with narrow framings concealing considerations which are important within broader discussions of AI intelligence. Drawing from interactive workshops conducted with AI practitioners, we explore practices of data acquisition, cleaning, and annotation, as the point where practitioners interface with domain experts and data annotators. Despite forming a crucial part of AI design and development, this type of data work is frequently framed as a tedious, unskilled, and low-value process. In exploring these practices, we examine the political role of the epistemic framings that underpin AI development and how these framings can shape understandings of distributed intelligence, labour practices, and annotators’ agency within data structures. Finally, we reflect on the implications of our findings for developing more participatory and equitable approaches to machine learning applications in the service of R-AI.

Social Media's Influence on Business Decision-Making: A Study of Communication Networks in Management Practices

The investigation reveals how business leaders use social media tools to make decisions while managing communication within their teams. This research analyzes how companies use social media to support their business strategies while studying its effects on important decisions and interactions between stakeholders. This study analyzes business decision making with support vector machines, decision trees, neural networks, and random forests to reveal patterns in social media data effectiveness. Analysis using neural networks shows a notable improvement in decision efficiency through social media data which achieves 85% accuracy rates. Random forests outperformed other algorithms by 10% when it came to customer behavior prediction while decision trees improved market response by 15%. Businesses that use social media for immediate customer interaction and team innovation adapt better to fast-changing markets. This study helps businesses better understand how social media works in today's market by providing tools to improve their communication methods and business decision making.

Hybrid Optimization-Based Sequential Placement of DES in Unbalanced Active Distribution Networks Considering Multi-Scenario Operation

The increasing penetration of distributed generation (DG) brings about great economic and environmental benefits, while also negatively affecting the operation of distribution networks due to its high intermittency. Although distributed energy storage (DES) can effectively deal with the problems caused by massive DG penetrations by decoupling the generation and consumption of electricity, the placement of DES significantly determines the effectiveness of its capabilities. Unfortunately, existing DES placement studies are commonly based on a balanced network model, whereas practical distribution networks are unbalanced. In addition, existing DES placement studies are mostly based on an extreme scenario and rarely consider the operational complexity resulting from the uncertainties of DGs and loads. To address the aforementioned challenges, this paper proposes a hierarchical and sequential DES placement strategy in distribution networks by considering multi-scenario operations. Specifically, the proposed hierarchical framework for DES placement includes three sequential layers: outer, inter, and inner. In the outer layer, a multi-scenario comprehensive loss sensitivity index (MSCLSI) is first introduced to search for the most effective DES placement location. Subsequently, the sizing and scheduling of DES for the selected location are conducted through coordinated optimization across the inter and inner layers, which can be solved using a hybrid method combining particle swarm optimization and second-order cone programming (PSO-SOCP). Finally, a series of detailed simulations are carried out over the IEEE-33 test system and the experimental results demonstrate that the proposed scheme can provide significant effectiveness and superiority compared to the state-of-the-art schemes.

Mobile urbanism from below: Afro-Christian churches as place-makers and scale-makers in European midsized cities

This article conceptualises Afro-Christian churches as vectors for the circulation of spatial knowledge. Scholarship on the ‘reverse mission’ of Afro-Christian churches to Europe emphasises their emplacement in global cities. Yet, during the last decades, new religious geographies have been produced, resulting in a dense, trans-urban network of Afro-Christian churches in Europe, covering not only global cities but also – and more dominantly – midsized cities. This article argues that along this emerging polycentric, trans-urban network of Afro-Christian churches place-making practices are exchanged that impact on and interconnect urban landscapes globally. Referring to recent advances in policy transfer literature, this phenomenon is conceptualised as a ‘mobile urbanism from below’. Apart from discussing the Afro-Christian place-making practices that are circulated by mundane transfer agents and the emergence of a ‘transnational vernacular’ that (invisibly) regenerates ‘the surrounds’ of cities worldwide from below, the article demonstrates how this ‘mobile urbanism from below’ has significant implications for the (subjective) scalar repositioning of cities. By developing the notion ‘mobile urbanism from below’, this article illustrates how (the spatialities and materialities of) European cities are made in relation to cities elsewhere and questions Eurocentric views within urban studies from within Europe itself.

Improving Blood Pressure Control and Tobacco Use Cessation Intervention In Primary Care: Protocol for the Alabama Cardiovascular Cooperative Heart Health Improvement Project.

Alabama has the second highest rate of cardiovascular disease (CVD) mortality of any US state and a high prevalence of CVD risk factors such as hypertension, diabetes, obesity, and smoking. Within the state, there are disparities in CVD outcomes and risk factors by race or ethnicity and geography. Many primary care practices do not have the capacity for full-scale quality improvement (QI) initiatives. The Alabama Cardiovascular Cooperative (ALCC), which includes academic and community stakeholders, was formed to support primary care practices to implement QI initiatives to improve cardiovascular health. The ALCC is implementing a Heart Health Improvement Project (HHIP) in primary care practices with suboptimal rates of blood pressure (BP) control and tobacco use screening. The study aimed to support primary care practices to increase BP control among adults with hypertension and increase rates of tobacco use screening and cessation intervention. We are using a type 1 hybrid design to test the effects of the HHIP on BP control among adults with hypertension and tobacco use screening and cessation intervention, while collecting information on implementation. Primary care practices were recruited through existing practice networks and additional electronic and in-person outreach. To ensure participation from a broad range of clinics, we required at least 50% of practices to be Federally Qualified Health Centers or look-alikes and to include representation from practices in rural areas. At baseline, we collected information about practice characteristics and preintervention rates of BP control and tobacco use screening and cessation intervention. The QI intervention includes quarterly activities conducted over a 12-month period. The HHIP uses a multipronged approach to QI, including practice facilitation and technical assistance, on-site and e-learning, and improvement through data transparency. We will conduct a pre-post analysis to estimate the effects of the HHIP and whether there is an enduring change in outcomes after the 12 months of HHIP activities beyond what would be expected due to secular trends. Practice recruitment took place between April 2021 and October 2022. After contacting 417 primary care practices, 51 were enrolled, including 28 Federally Qualified Health Centers or look-alikes; 47 practices implemented the HHIP. Among 45 practices that completed the baseline survey, 11 (24%) were solo practices, while 28 (62%) had 1-5 clinicians, and 6 (13%) had 6 or more clinicians. The median number of patient visits per year was 5819 (IQR 3707.3-8630.5). Practices had been in operation for a mean of 19.2 (SD 13.0) years. At baseline, the mean BP control rate was 49.6% and the rate of tobacco use screening and cessation intervention was 67.4%. If successful, the ALCC and HHIP may improve the implementation of evidence-based guidelines in primary care and, subsequently, cardiovascular health and health equity in the state of Alabama. DERR1-10.2196/63685.

The impact of pharmacogenomics on a pharmacy and therapeutic committee's formulary system management: An opinion of the pharmacokinetics/pharmacodynamics/pharmacogenomics practice and Research Network for the American College of Clinical Pharmacy

The impact of pharmacogenomics on a pharmacy and therapeutic committee's formulary system management: An opinion of the pharmacokinetics/pharmacodynamics/pharmacogenomics practice and Research Network for the American College of Clinical Pharmacy

Medication therapy management: The evolution of clinical pharmacy services in U.S. Community‐Based Pharmacy Practice—An opinion of the community‐based practice and research network of the American College of Clinical Pharmacy

AbstractPharmacists in all settings are continually expanding their roles and responsibilities and advocating for the profession. Pharmacists, as medication experts, are trained to identify and resolve medication therapy problems. The act of providing clinical pharmacist expertise is not tied to a specific practice setting, but rather is the mindset in which a pharmacist approaches patient‐centered practice. In community‐based pharmacy settings, pharmacists have been providing clinical services for years through Medication Therapy Management (MTM) and other enhanced services which prioritize quality care. This manuscript will outline the history of the pharmacist's role in managing patients utilizing MTM, present evidence of positive patient care outcomes from pharmacist‐led services in the community‐based setting, and provide recommendations for United States payers to acknowledge and increase payment for pharmacist‐led services. This paper represents the opinion of the Community‐Based Practice and Research Network of the American College of Clinical Pharmacy (ACCP) in the United States. It does not necessarily represent an official ACCP commentary, guideline, or statement of policy or position.

Vehicle Mass Estimation via Practical Supervisory Artificial Neural Networks Using Perturbed Engine Torque and Acceleration Inputs

Various model-based mass estimation approaches have been discussed for a long time. However, estimation performance often deteriorates in some driving situations and, in particular, slow convergence and excessive overshoot of estimates are a major issue for model-based approaches. Meanwhile, mass estimation approaches using ANN models have recently emerged to propose better solutions, but their usefulness has not been fully investigated. Therefore, this paper presents a vehicle mass estimation strategy using practical supervisory artificial neural networks to achieve more accurate results with better convergence. Here, the perturbed engine torque and vehicle longitudinal acceleration are selected as the inputs of the ANN (instead of the original engine torque and vehicle acceleration), which allows for the faster convergence of estimates with high accuracy; these inputs are existing sensor values already available in the vehicle system. The effectiveness of the proposed ANN approach was verified using simulation and software-in-the-loop simulation (SILS) with field test data, and it was found that the convergence speed of the proposed ANN is almost twice as fast as that of the model-based approach, the accuracy is much better, and the estimation quality is constantly stable without any excessive transient responses. This study will provide further insights into mass estimation using the ANN approach.

Practice networks in Germany: structure, cooperation and priorities

The outpatient sector in Germany is characterised by special features, including organisational separation from the inpatient sector, the free choice of doctor for patients and the lack of standardised communication channels between service providers (1). This makes coordination and continuity of care more difficult, which can ultimately lead to cuts in the quality of care for patients. Examples of negative consequences of uncoordinated care include excessive utilisation of healthcare services, interruptions to treatment pathways or incorrect medication. In particular, multimorbid patients with a high demand for healthcare services are at risk of inappropriate care. There is potential for development with regard to collaborative professional practice in the outpatient sector and care models are needed that improve cooperation and coordination in care.

Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains

We implement a set of simulation experiments in NetSquid specifically designed to estimate the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations that are not currently available in other tools and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and routing protocols. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially and a single switch equipped with multiple memory registers that is able to handle multiple swapping requests.

Explainable hybrid ensemble model for fault diagnosis with imbalanced data in practical optical networks

Explainable hybrid ensemble model for fault diagnosis with imbalanced data in practical optical networks

A thin film lithium niobate near-infrared platform for multiplexing quantum nodes

Practical quantum networks will require multi-qubit quantum nodes. This in turn will increase the complexity of the photonic circuits needed to control each qubit and require strategies to multiplex memories. Integrated photonics operating at visible to near-infrared (VNIR) wavelength range can provide solutions to these needs. In this work, we realize a VNIR thin-film lithium niobate (TFLN) integrated photonics platform with the key components to meet these requirements, including low-loss couplers (<1 dB/facet), switches (>20 dB extinction), and high-bandwidth electro-optic modulators (>50 GHz). With these devices, we demonstrate high-efficiency and CW-compatible frequency shifting (>50% efficiency at 15 GHz), as well as simultaneous laser amplitude and frequency control. Finally, we highlight an architecture for multiplexing quantum memories and outline how this platform can enable a 2-order of magnitude improvement in entanglement rates over single memory nodes. Our results demonstrate that TFLN can meet the necessary performance and scalability benchmarks to enable large-scale quantum nodes.

Optimal sizing and allocation of multiple distribution generations for radial distribution networks enhancement using advanced metaheuristic-based osprey optimization algorithm

Abstract Due to transmission over a long distance from the point of generation to the consumer residence, the distribution station is faced with various challenges which include but not limited to high-power losses, voltage instability and voltage deviation. Thus, it becomes imperative to mitigate these. In this paper, a metaheuristic optimization approach called Osprey Optimization Algorithm (OOA) is proposed to evaluate the best sites and sizes of multiple Distributed Generations (DGs) on radial distribution networks. The suggested OOA is inspired based on the intelligent conduct of ospreys during hunting. The incorporation of multiple DGs at unity, 0.95, optimal power factor was evaluated, and various scenarios were examined. The effectiveness of OOA was scrutinized on the standard IEEE 33-bus distribution system and validated on a practical distribution network, Ayepe 11 KV 34- bus feeder of the Ibadan Electricity Distribution Company (IBEDC). The results obtained from the simulations depicts that three DG units operating at optimal power factor produced the best outcome when considering total active power loss (TAPL) and total reactive power loss (TRPL) on both networks. A 92.87% reduction on TAPL as well a 91.60% reduction on TRPL was attained on the 33-bus system while on the Ayepe 34-bus system both the TAPL and TRPL reduced by 98.51% when juxtaposing them with their respective base cases. The outcomes obtained corroborate the efficacy and superiority of OOA when comparing them to outcomes from recent optimization approach.

Channel noise induced stochastic effect of Hodgkin-Huxley neurons in a real classification task.

Noise is generally considered to have negative effects on information processing performance. However, it has also been proven that adding random noise or a certain level of stochastic (random) variability to a nonlinear system can increase its performance or sensitivity to weak signals. Despite the studies on this concept, called stochastic resonance in computational neuroscience, this phenomenon is still among the topics that need detailed research, especially in machine learning. In this study, the effect of noise arising from the intrinsic dynamics of the neurons forming the network in a spiking neural network consisting of Hodgkin-Huxley neurons on the image classification success of the network is investigated. In the first part of this two-part study, a practical neural network model consisting of Hodgkin-Huxley neurons is proposed and the network is tested in a 4-class real classification task. It is observed that the network consisting of Hodgkin-Huxley neurons has a classification performance at least as successful as the artificial neural network. In the second part of the study, the neurons in the network are replaced with stochastic Hodgkin-Huxley neurons, which more realistically represent the biological neuron, and the classification performance of the network at different cell membrane sizes is examined. Findings reveal that a spiking network consisting of stochastic Hodgkin-Huxley neurons, in which intrinsic noise dynamics are incorporated into the system, shows maximum classification performance at an optimal intrinsic noise level. It is called this reflection observed in the classification performance of a spiking network, which is referred to as stochastic resonance in computational neuroscience, as stochastic classification resonance in this study. This study also highlights the importance of bridging the gap between biological neuroscience and artificial neural networks for a better understanding of neurological structure.

An Anonymous and Efficient Authentication Scheme with Conditional Privacy Preservation in Internet of Vehicles Networks

The Internet of Vehicles (IoV) is an emerging technology that enables vehicles to communicate with their surroundings, provide convenient services, and enhance transportation systems. However, IoV networks can be vulnerable to security attacks because vehicles communicate with other IoV components through an open wireless channel. The recent related work suggested a two-factor-based lightweight authentication scheme for IoV networks. Unfortunately, we prove that the related work cannot prevent various security attacks, such as insider and ephemeral secret leakage (ESL) attacks, and fails to ensure perfect forward secrecy. To address these security weaknesses, we propose an anonymous and efficient authentication scheme with conditional privacy-preserving capabilities in IoV networks. The proposed scheme can ensure robustness against various security attacks and provide essential security features. The proposed scheme ensures conditional privacy to revoke malicious behavior in IoV networks. Moreover, our scheme uses only one-way hash functions and XOR operations, which are low-cost cryptographic operations suitable for IoV. We also prove the security of our scheme using the “Burrows–Abadi–Needham (BAN) logic”, “Real-or-Random (ROR) model”, and “Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool”. We evaluate and compare the performance and security features of the proposed scheme with existing methods. Consequently, our scheme provides improved security and efficiency and is suitable for practical IoV networks.

A machine learning approach to quantify effects of geometric design features and traffic control devices on wrong-way driving incidents at partial cloverleaf interchange terminals

This study addresses the issue of wrong-way driving (WWD) incidents at partial cloverleaf (parclo) interchange terminals in the United States. These incidents are a safety concern, often attributed to geometric design features and inadequate traffic control devices (TCDs). While previous research has acknowledged the significance of parclo interchanges as common initial entry points for WWD crashes, few studies have comprehensively quantified the impact of TCDs and design features on recurring WWD incidents.In response, this study collected data from 75 parclo interchange terminals spanning 13 states. A subset of 28 ramp terminals exhibiting recurrent WWD incidents, amounting to 410 incidents during the data collection phase, received focused attention. Leveraging modern machine learning methodologies, two techniques: eXtreme Gradient Boosting (XGboost) and Lasso-logistic regression were applied to quantify the influence of distinct TCDs and design features on the probability of WWD incidents occurring.The outcomes of this analysis revealed noteworthy results: the fitted XGboost model displayed an average accuracy of 80%, closely followed by the fitted Lasso-logistic regression model with an average accuracy of 78%. These models were subsequently employed to construct a practical network screening tool. This tool assists in the identification of potential WWD incident locations, predicated on the effects of TCDs and design characteristics.The significance of this study lies in its potential to inform and guide state and local transportation agencies in enhancing the safety of parclo interchange terminals. By discerning the impacts of TCDs and design features, stakeholders can implement improvements that curtail the occurrence of WWD incidents, contributing to enhanced road safety and optimized traffic management.

Two-stage optimization strategy for the active distribution network considering source-load uncertainty

This study aims to advance the development of the active distribution network (ADN) by optimizing resource allocation across different stages to enhance overall system performance and economic benefits. First, an ADN optimization model is constructed based on a two-stage robust optimization approach. The first stage focuses on determining optimal decision variables within the uncertainty set, while the second stage adjusts control variables based on the initial stage decisions. This model effectively addresses source-load uncertainties while preserving the flexibility and adaptability of decision-making solutions. Additionally, this study explores uncertainty models that incorporate correlation factors. The IEEE33-node model is employed to validate the effectiveness and superiority of the proposed optimization strategy through numerical simulations. Simulation results demonstrate that Model 3 comprehensively accounts for photovoltaic and wind turbine generator planning by optimizing their capacity configurations, leading to a 23% increase in distributed generation (DG) penetration. During high-load periods (e.g., 13:00 and 16:00), DG output reaches 47% and 50% of the demand load, underscoring the critical role of DG in supporting the power grid during peak hours. Overall, the proposed two-stage optimization strategy considers source-load uncertainties, significantly reducing economic costs, enhancing DG output, and improving overall system performance. In scenarios with correlated uncertainties, the optimized results exhibit greater accuracy and reliability, providing robust support for the planning and operation of practical distribution networks.

Optimizing energy efficiency in buildings’ cold water systems: A differential pressure control-based global approach

Buildings’ cold water systems have become increasingly complex, with independent equipment control leading to greater energy consumption and greenhouse gas emissions. To improve control efficiency and maximize energy savings, simulation and experimental studies of these systems are essential, although full-scale experimental setups are rare. This paper employs FloMASTER modeling to establish a large-scale practical pipeline network system for validating models, evaluating strategies, and optimizing energy efficiency. The modeling method is validated as accurate, with normalized mean bias error (NMBE) and coefficient of variation of the root mean square error (CVRMSE) values for nodal pressure and flow distribution meeting established evaluation criteria. A quantitative evaluation of constant differential pressure control strategies, considering hydraulic and thermal characteristics as well as energy consumption, reveals that the strategy based on intermediate loop users performs best. This strategy achieves an average hydraulic imbalance rate of only 5.05%, the quickest hydraulic stabilization and restoration time, and comparable secondary pump energy consumption across three control strategies. Furthermore, this simulation study proposes a global combined control strategy that enables more stable operation of the system, reduces chiller energy consumption by 4.66%, secondary pump energy consumption by 9.93%, and overall system energy consumption by approximately 5.38%. These findings suggest that this methodology can be applied to large-scale pipeline networks in complex cold water systems, yielding substantial energy savings.

K-means Optimized Network Security Management in the Internet Context

The rapid development of the Internet has had a broad and profound impact on humanity, making information acquisition and dissemination more convenient. It has also brought significant opportunities and benefits to business and the economy. However, there are some issues, such as personal factors and data security concerns. In order to solve the above problems, the K-means algorithm is optimized from the perspectives of K-value validity index, feature weighting and three-branch decision making. First, the optimal clustering results are determined according to K-value validity index, and the influence of different dimensional features on clustering is considered for feature weighting, and the uncertain objects in the three-branch decision deadlock class are divided into the boundary domain. The delay decision of the boundary domain data is carried out, and the K-means clustering optimization algorithm is improved by combining the above three aspects, and the intelligent network security management system is developed on this basis. The results showed that the K-means optimization algorithm achieved the highest average accuracy rate, adjusted Morandi index, and adjusted mutual information across various datasets, with values of 96.01%, 0.866, and 0.869, respectively. In practical network attack scenarios, the K-means optimization algorithm attained an attack threat recognition accuracy of 94.38%. Under unknown network attack types, its detection rate and false alarm rate were 94.63% and 1.32%, respectively. Surveys conducted post-implementation of the intelligent network security management system indicated that over 90% of users were satisfied with their experience of the system. In summary, the proposed method accurately identifies potential network threats in network data, fulfilling performance requirements for network security management systems and ensuring the security of network resources.