Optimal Utilization Research Articles

Immunofluorescence Use and Techniques in Glomerular Diseases – A Review

Background: Immunofluorescence (IF) studies play an essential role in the evaluation of medical renal biopsies. Particularly, in the study of renal glomerular diseases, where it provides fundamental data for the diagnosis, classification, and etiology of the glomerular pathologies. Diverse techniques may be used to optimize the utilization of IF studies, from variations on the test methodologies to expertise on the interpretation of the results and knowledge of potential pitfalls. Summary: This manuscript presents a brief review on the history of IF and its utilization in kidney pathology, followed by a description of the IF methods, including the use of immunofluorescence on paraffin embedded tissue (paraffin IF), and other novel techniques. Guidelines on how to best report IF findings are reviewed, along with a description of antibodies commonly used in glomerular diseases, highlighting their distribution within the normal kidney and potential pitfalls in interpretation. Finally, the use and interpretation of IF is discussed in more detail in individual entities on a range of glomerular diseases. Key Messages: Immunofluorescence is crucial for interpretation of renal biopsies and diagnosis of glomerular diseases. Knowledge of IF techniques, alternative procedures, its use and proper interpretation is essential for optimal utilization of IF in renal pathology, and this review proposes to serve as a simplified and practical guide on this topic.

Spatial change under the institution of land rights: A research note on the double-tragedy in the Wanhua Railway Station redevelopment zone, Taipei

Equalization of land rights is a noble goal for societies to pursue. The reform of farmland to the tiller has significantly transformed Taiwan’s agricultural economy and paved the way for rapid industrialization. Nevertheless, market-led equalization of urban land rights may result in unfavorable effects that prevent scarce urban land resources from optimal utilization. The research of Wanhua Railway Station redevelopment zone has discovered the phenomenon of the double-tragedy of the commons and anti-commons. Fragmented land rights hamper redevelopment efforts and induce illegal construction instead. Double-tragedy leads to social segregation that amplifies inequality between urban neighborhoods.

Implementation of PPCA Imputation, SMOTE-N Class Balancing in Hepatitis Classification Using Naïve Bayes

The availability of complete data in research is crucial, especially in the initial stages. The Hepatitis data used in this study encountered issues such as missing data and class imbalance, which hindered its optimal utilization. The method employed to address missing data was the PPCA imputation method. After filling in the missing data, the data was balanced using the SMOTE-N class balancing method and classified using Gaussian Naïve Bayes. The aim of this research was to compare the classification evaluation of hepatitis disease using Naive Bayes with the PPCA imputation approach and SMOTE-N class balancing. The best results from each scenario yielded an AUC value of 0.833 in the first scenario with an 80:20 data split for training and testing, and 0.875 in the second scenario with a 90:10 data split. The highest AUC value was obtained in the application of PPCA imputation with SMOTE-N class balancing using Naive Bayes classification. This demonstrates that the implementation of PPCA imputation with SMOTE-N class balancing has a better impact on the performance of Naïve Bayes classification.

What are the minimal criteria of goal-directed perfusion (GDP) in adult cardiac surgery?

Introduction: Nowadays, many cardiac surgery teams are adopting the goal-directed perfusion (GDP) strategy in their practice to maintain optimal perfusion during cardiac surgeries and improve patient outcomes. Furthermore, it plays an indispensable role as a quality control tool to monitor both perfusionists’ practice and equipment (in addition to disposables used in the surgery. Objectives: To identify the role and the elements of GDP that facilitate better surgical outcomes. In addition, to compare between GDP and the traditional conventional perfusion strategy (CP) in terms of perfusion adequacy and surgical outcomes. Finally, to recognize challenges that may prevent the effective application of GDP and finding possible applicable solutions. Methods: A systematic literature review was conducted from three different databases PubMed, SpringerLink, and ScienceDirect. The selected studies were in English from the USA and Europe with a time frame starting from 2005. Results: The findings highlight the crucial role of the GDP strategy in protecting and preserving end-organ function after on-bypass cardiac surgery procedures. Furthermore, a clear understanding of the GDP implementation component and criteria was obtained. Alternatively, a satisfactory GDP level can be achieved by the optimal utilization of available resources. Discussion: Dismantling the GDP strategy into practically recognized components to ease the implementation at different levels of perfusion practice. Conclusion: The GDP approach involves the intensive monitoring of respiratory-related parameters to enhance surgical outcomes. The process includes blood preservation, optimal flow and intraoperative parameters management. Monitoring is the key element of GDP, which can be applied by using the sophisticated technology or the proper use of existing resources to develop protocols within the international guidelines and recommendations. Achieving an optimal perfusion requires concerted efforts of organizational, safety, and practical measurements.

Heterogeneous Online Computational Platform for GEM-Based Plasma Impurity Monitoring Systems

The fusion energy research field presents many intricate challenges that require resolution. Many diagnostic systems employed in experiments are approaching the limits of current technology. Implementing efficient measurements requires using an appropriate set of tools to facilitate the optimal utilization of hardware. Fusion energy measurements must provide low latency processing with the capacity for future improvements and the ability to handle complex data flows efficiently. The presented work addresses these requirements and describes the implementation of a high-performance, low-latency software platform with convenient development for soft X-ray (SXR) plasma impurities emission tracing—the Asynchronous Complex Computation Platform (AC2P). This article presents the architectural design, implementation details, and performance and latency measurements based on the raw data acquired from the WEST tokamak and laboratory tests. AC2P provides the tools to develop low-latency, multi-core, multi-device complex data flow graph scale-up solutions for measuring impurities in hot plasmas. The system has been designed to operate online during experiments, calculate the energy distribution, position and occurrence time of SXR photons, monitor the data stream’s quality and archive any abnormalities for subsequent offline verification and algorithm improvement. This article presents AC2P, which operates as part of the SXR measurement system on the WEST tokamak.

Multi-agent based optimal sizing of hybrid renewable energy systems and their significance in sustainable energy development

This paper delves into the enhancement and optimization of on-grid renewable energy systems using a variety of renewable energy sources, with a particular focus on large-scale applications designed to meet the energy demand of a certain load. As global concerns surrounding climate change continue to mount, the urgency of replacing traditional fossil fuel-based power generation with cleaner, more cost-effective and dependable alternatives becomes increasingly apparent. In this context, a comprehensive investigation is conducted on grid connected hybrid energy system that combines photovoltaic, wind, and fuel cell technologies. The study employs three state-of-the-art optimization algorithms, namely Walrus Optimization Algorithm (WaOA), Coati Optimization Algorithm (COA), and Osprey Optimization Algorithm (OOA) to determine the optimal system size and energy management strategies, all aimed at minimizing the cost of energy (COE) for grid-based electricity. The results of the optimization process are compared with the results obtained from the utilization of the Particle swarm optimization (PSO) and Grey Wolf optimizer (GWO). The findings of this study underscore both the practical feasibility and the critical importance of adopting on-grid renewable energy systems to decrease the dependence on traditional energy sources within the grid. The proposed WaOA succeeded to reach the optimal solution of the optimal design process with a COE of 0.51758129611 $//kwh while keeping the loss of power supply probability (LPSP), the reliability index, at 7.303681e-19. The practical recommendations and forward-looking insights provided within this research hold the potential to foster sustainable development and effectively mitigate carbon emissions in the future.

Revolutionizing Supply Chain : The Synergy of Warehouse Management and Data Analytics

This article examines the transformative impact of integrating data analytics with warehouse management in modern supply chains. It explores how this synergy is revolutionizing inventory management, order fulfillment, and customer service across industries. The article discusses key elements of effective warehouse management, including optimal space utilization, efficient inventory control, and timely order processing. It then delves into how data analytics elevates these functions through inventory optimization, real-time monitoring, and predictive analytics. Case studies from e-commerce and manufacturing sectors illustrate real-world applications and benefits. Finally, the competitive advantages gained through enhanced operational efficiency, cost reduction, and improved service levels are analyzed, highlighting the critical role of this integration in shaping the future of supply chain management.

Two Erigeron species comparison based on their ingredient profile by UPLC-PDA-QTOF-MS/MS and discriminant analysis.

Erigeron breviscapus (EB) and Erigeron multiradiatus (EM) are the two species of the genus Erigeron (Asteraceae) with extremely close genetic relationships. They were used as the same "meiduoluomi" for the treatment of plague and epidemics in traditional Tibetan medicine. But in traditional Chinese medicine, only EB is used for treatment of cerebrovascular obstruction, hemiplegia due to stroke, coronary artery obstruction, chest congestion, and angina pectoris. These two Erigeron species show different effects in different traditional medicine systems. Therefore, analyzing the chemical compositions of two species will not only enhance comprehension of their medicinal properties but also foster the advancement and exploration of novel applications. However, to date, there has been no comprehensive and detailed investigation comparing the constituents of EB and EM. A methodology for rapid identification of chemical profiles from two Erigeron species was devised through the integration of ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS/MS) and multivariate statistical analysis. Additionally, a UHPLC-photo-diode array (PDA) method was established to precisely quantify of 11 components. A total of 58 constituents comprising flavonoids, phenolic acids, saponin, and long chain fatty acids were elucidated. Thirteen compounds were identified as potential differentiators in chemical profiles among the two Erigeron species. For quantitative assessment, 11 bioactive compounds were simultaneously quantified across 49 batches of Erigeron species samples utilizing UHPLC-PDA with wavelengths of 325, 254, and 266 nm. The method demonstrated excellent precision, linearity, accuracy, repeatability, stability, and recovery. The findings from this study will serve as a reference for quality control, functional activity exploration, and improved clinical application based on the ingredient profiles of the two species. Furthermore, this inaugural investigation into the ingredient profiles of these two species will enhance the potential and optimal utilization of both EB and EM resources.

Exergy efficiency improvement by compression heat recovery for an integrated natural gas liquefaction-CO2 capture-NGL recovery process

For liquefied natual gas production, cryogenic distillation offers lower thermal energy consumption and a more compact footprint for carbon capture unit than the widely used chemical absorption. However, the reduction in thermal energy consumption comes at the cost of increased power consumption. To reduce the power consumption of cryogenic distillation based natural gas lqiuefaction plants, an integrated process for liquefied natual gas production, cryogenic CO2 capture and natural gas liquids recovery is proposed, which is called the base process. This base process is further integrated with a heat recovery unit (Organic Rankine cycle or Kalina cycle) to investigate the optimal utilization of compression heat in the system. The proposed processes are modeled in Aspen HYSYS and optimized using a genetic algorithm installed in Matlab. The results show that specific power consumption of the base case is 0.385 kWh/Nm3 NG. Due to the lack of compression heat recovery, significant exergy loss occurs at the water coolers, accounting for approximately 37 % of the total system exergy destruction. Among the evaluated methods, the supercritical Organic Rankine cycle, using R1234ze as the working fluid, recovers approximately 60 % of the exergy from compression heat with an 8.7 % improvement in system exergy efficiency. The subcritical Organic Rankine cycle, using R600a as the working fluid, achieves a slightly lower exergy efficiency improvement of 7.1 %. The Kalina cycle, while less efficient, still improves the system’s exergy efficiency by 6.3 %. Economic analysis reveals that the total annualized cost of the base process is $8,441,416, with a levelized cost of $0.1411/kg. The subcritical Organic Rankine Cycle provides the most cost-effective solution, with the lowest total annualized cost of $8,305,154 and a levelized cost of $0.1402/kg. The Kalina cycle ranks second in cost-effectiveness, with a total annualized cost of $8,345,914 and a levelized cost of $0.1405/kg NG. Although the supercritical ORC requires the highest capital investment, it proves more cost-effective than the base process due to its reduced annual operating costs.

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Global meta-analysis and machine learning reveal the critical role of soil properties in influencing biochar-pesticide interactions

Biochar application in soils is increasingly advocated globally for its dual benefits in enhancing agricultural productivity and sequestering carbon. However, lingering concerns persist regarding its environmental impact, particularly concerning its interactions with pesticide residues in soil. Previous research has fragmentarily indicated elevated pesticide residues and prolonged persistence in biochar-amended soil, suggesting a potential adverse consequence of biochar application on pesticide degradation. Yet, conclusive evidence and conditions for this phenomenon remain elusive. To address this gap, we conducted a comprehensive assessment using meta-analysis and machine learning techniques, synthesizing data from 58 studies comprising 386 observations worldwide. Contrary to initial concerns, our findings revealed no definitive increase in pesticide concentrations in soil following biochar application. Moreover, a significant reduction of 66 % in pesticide concentrations within soil organisms, such as plants and earthworms, was observed. The quantitative analysis identified soil organic matter content as a key factor influencing biochar-pesticide interactions, suggesting that applying biochar to soils rich in organic matter is less likely to increase pesticide persistence. This study provides a critical assessment of the environmental fate of pesticides under biochar application, offering valuable guidance for the optimal utilization of both pesticides and biochar in sustainable agricultural practices.

Mechanical Expertise Management for the Information Management inScaling Systems with Deep Learning Process

Dynamic scaling information management refers to the adaptive process of adjusting resources and managing data in real time to meet varying demands in computational and storage environments, particularly in cloud computing and data-intensive applications. This approach ensures optimal performance and resource utilization by automatically allocating or deallocating computing power, storage capacity, and network bandwidth based on current workloads and system performance metrics. Key components include monitoring tools that continuously assess resource usage, predictive analytics to anticipate future demands, and automated orchestration systems that execute scaling actions without human intervention. This paper introduces Predicted Dynamic Scaling in Information System Development (PDS-ISD) as a novel framework for talent cultivation within ISD education. By synthesizing empirical observations and theoretical models, PDS-ISD offers a predictive tool to forecast talent cultivation outcomes based on key educational factors. Through a comprehensive review of literature, the paper explores the theoretical foundations and practical applications of PDS-ISD in ISD education. Additionally, simulated results demonstrate the effectiveness of PDS-ISD in predicting talent outcomes across various scenarios. The findings highlight the importance of factors such as curriculum design, instructor expertise, student readiness, and project demands in shaping talent cultivation outcomes. The paper concludes with implications for practice and future research directions to further enhance the predictive capabilities and applicability of PDS-ISD in ISD education. Additionally, simulated results demonstrate the effectiveness of PDS-ISD in predicting talent outcomes across various scenarios, with an average accuracy of 85%. The findings highlight the importance of factors such as curriculum design, instructor expertise, student readiness, and project demand in shaping talent cultivation outcomes

Numerical modeling and parametric analysis of temperature distribution in soil based on water content variation under radio frequency heating for in-situ thermal remediation

Radio frequency heating (RFH) is recognized as an efficient and economical method for volatilizing organic pollutants from contaminated soils. Although some numerical simulations have been conducted to predict temperature changes during RFH, models that account for effects of water content (WC) variation on dielectric properties and temperature evolution in soil were rarely reported. To address this, a three-dimensional numerical model integrating electromagnetic-thermal conversion, heat transfer, and mass transfer was developed with coupling dynamic changes in WC and corresponding impacts on soil properties. The effects of soil types and RFH engineering parameters on temperature evolution and energy utilization were evaluated. The model's temperature prediction was validated using experimental data, indicating that temperature decreases outward from hot electrode, with high-temperature regions forming at the top and bottom regions near electrode. Water evaporation in soil causes a temperature plateau with >76.25 % of energy utilized for heating and evaporating water during 60-d RFH. Moreover, higher soil WC could increase electromagnetic-thermal conversion capability, counteracting the negative impact of increased specific heat capacity on temperature rise. Thermophysical properties of soil are crucial in RFH: sand, with lower specific heat capacity and higher thermal conductivity, results in faster heating rates. While, clay, with higher density and specific heat capacity, results in lower heating rates but greater energy utilization efficiency. Low-power RHF and wider well spacing improve the uniformity of temperature distribution but require more time to reach target temperatures. Moreover, input power influences the magnitude and timing of optimal energy utilization efficiency, while well spacing primarily affects timing. The models developed in this study provide essential guidance for RFH-based soil remediation projects.

Implementation of Risk Management in Asset Management Governance Towards Optimizing Asset Utilization at Public Service Agency State Universities (Case Study of UPN Veteran Yogyakarta)

This study aims to analyze the application of risk management in asset governance at UPN Veteran Yogyakarta to achieve optimal asset utilization. As a Public Service Agency State University (PTN-BLU), UPN Veteran Yogyakarta faces various challenges in asset management, including budget constraints, lack of consistent monitoring, and the unavailability of formal risk evaluation standards. Qualitative descriptive research methods are used to gain in-depth understanding through interviews with asset managers and field observations. The results show that the implementation of risk management can have a significant impact on the efficiency of budget allocation, which includes more appropriate budget distribution based on priority needs and risks. Risk management also has the potential to extend the useful life of assets, with risk mitigation measures taken on assets that show signs of wear or early damage, thus preventing major repair costs due to serious damage. This research highlights that a technology-based monitoring system is an indispensable aspect to support the effectiveness of risk management in asset management. The implementation of structured risk assessment procedures helps in determining asset maintenance priorities, so that the assets that are most critical to the sustainability of campus operations receive greater attention. With the implementation of this comprehensive risk management, optimization of asset utilization can be achieved, which in turn supports the stability and sustainability of UPN Veteran Yogyakarta's operations as a quality higher education institution.

Anatomical Structure and Bending Properties of Calamus zollingeri

The relationship between the structure and mechanical properties of materials has been a hot topic. However, there is a lack of research on the relationship between the structure of rattan and its mechanical properties. This study investigated the anatomical structure and bending properties of Calamus zollingeri, a commercially important rattan species. Samples were collected from different radial positions of the stem and analyzed for their microstructural features and mechanical properties. The distribution and morphology of vascular bundles (VBs), parenchyma cells (PCs), vessels, and fibers were examined using light microscopy. Bending tests were conducted to determine the modulus of rupture (MOR) and modulus of elasticity (MOE). Stepwise linear regression analysis was employed to explore the relationship between structural features and mechanical properties. The results showed that the diameters, lengths and distribution densities of the vessel elements were 280.32 μm, 2163.56 μm, and 3.68 pcs/mm2, respectively; the double wall thickness, lumen diameter, diameter, length, length–width ratio were 7.55 μm, 4.30 μm, 11.85 μm, 1569.39 μm, and 134.08, respectively; the tissue ratios of the vessel elements, fibers, sieve elements, and PCs were 22.76%, 20.5%, 4.83%, and 51.87%, respectively. The MOR of C. zollingeri was 55.77 MPa, while the MOE was 2669.11 MPa. The MOR of C. zollingeri was mainly affected by a double wall thickness of fiber and the tissue ratio of PCs, while the MOE was mainly affected by a double arm thickness of fiber and the tissue ratio of the vessel elements. This research provides valuable insights into the structure–property relationships of rattan, which can inform its optimal utilization in various applications.

Modeling and solving time-sensitive task allocation for USVs with mixed capabilities

Efficient task allocation for Unmanned Surface Vehicles (USVs) in maritime operations is crucial for optimal resource utilization and mission success. This paper introduces a task assignment model that accounts for the capabilities and time constraints associated with the heterogeneity of USVs. An Extended-Restriction Multiple Traveling Salesmen Problem (ER-MTSP) model is formulated, encapsulating the diverse capabilities of USVs and time restrictions. To solve this problem, a Fuzzy Enhanced Non-Dominated Sorting Genetic Algorithm (FENSGA-II) is developed, incorporating adaptive random testing initialization and customized hierarchical operators to generate high-quality Pareto frontiers. The fuzzy-linguistic satisfactory degree then re-evaluates the linguistic importance preferences of the objectives within the Pareto set, aiding in reasonable decision-making. Additionally, a two-phase refinement strategy balances individual task values and structure topology, enabling flexible task selection in emergency situations. Simulations and lake trials conducted across various problem variants demonstrate the effectiveness of the model. The results highlight the superiority of our approach compared to current combinatorial optimization methods, providing enhanced solutions across different problem variations.

Effect of Architecture and Inference Parameters of Artificial Neural Network Models in the Detection Task on Energy Demand

Artificial neural network models for the task of detection are used in many fields and find various applications. Models of this kind require adequate computational resources and thus require adequate energy expenditure. The increase in the number of parameters, the complexity of architectures, and the need to process large data sets significantly increase energy consumption, which is becoming a key sustainability challenge. Optimization of computing and the development of energy-efficient hardware technologies are essential to reduce the energy footprint of these models. This article examines the effect of the type of model, as well as its parameters, on energy consumption during inference. For this purpose, sensors built into the graphics card were used, and software was developed to measure the energy demand of the graphics card for different architectures of YOLO models (v8, v9, v10), as well as for different batch and model sizes. This study showed that the increase in energy demand is not linearly dependent on batch size. After a certain level of batch size, the energy demand begins to decrease. This dependence does not occur only for n/t size models. Optimum utilization of computing power due to the number of processed images for the studied models occurs at the maximum studied batch size. In addition, tests were conducted on an embedded device.

Gereja Katolik dan Pengentasan Pengangguran Pemuda di Era Bonus Demografi

Youth unemployment in Indonesia remains notably high at 9.25%, posing a potential threat to the optimal utilization of the demographic dividend anticipated to peak by 2030. This research seeks to explore how the Catholic Church can contribute to addressing youth unemployment during this critical period. By employing the theoretical framework of Catholic social teaching—focusing on the principles of human dignity, solidarity, and integral justice—the study examines the Church's role, potential, and concrete opportunities in mitigating youth unemployment, and provides recommendations for key programs. A qualitative approach is used, involving a literature review of Catholic social teaching documents and analysis of secondary data. The study's findings suggest that the Catholic Church holds strategic potential through its extensive network of parishes and institutions to assist in resolving the youth unemployment issue. Suggested priority programs include skills training, educational scholarships, job fairs, the establishment of youth empowerment institutions, vocational schools, research centers, and business incubators. Effective cross-sector collaboration is crucial to ensuring that these Church-initiated programs function efficiently and offer long- term benefits to Indonesian youth, helping them seize the opportunities presented by the 2030 demographic bonus. This research adds to the understanding of the potential contributions of religious institutions in addressing contemporary socio-economic challenges and opens avenues for further study on the intersection of religion, public policy, and economic development within the framework of Indonesia‘s demographic dividend.

Advanced Li-S Battery Configuration Featuring Sulfur-Coated Separator and Interwoven rGO/CNT Fabric Current Collector.

The development of lithium-sulfur batteries (LSBs) marks a crucial milestone in advancing energy storage solutions essential for sustainable energy transitions. With high theoretical specific capacity, cost-effectiveness, and reduced ecological footprint, LSBs promise to enhance electric vehicle ranges, extend portable electronics' operational times, and stabilize grids integrated with renewable energy. However, challenges like complex processing, electrode instability, and poor cycling stability hinder their commercialization. This study introduces a novel battery design that addresses these issues by coating sulfur directly onto the separator instead of the current collector, demonstrating that active sulfur can be effectively utilized without being incorporated into the electrode structure. Using an interwoven substrate made from carbon nanotube (CNT) fabric adorned with reduced graphene oxide (rGO), this setup enhances manufacturing scalability, supports optimal sulfur utilization, and improves battery performance. The rGO decoration provides multiple highly conductive polysulfide trapping sites, enhancing active material reutilization, while the flexibility and mechanical strength of CNT fabric contribute to electrode integrity. This combination boosts electrical conductivity and polysulfide-capturing capability, effectively managing migrating sulfur species during charge-discharge cycles and mitigating sulfur loss and polysulfide shuttling. The results demonstrate superior cycling stability and efficiency, highlighting the potential of this approach in advancing LSB technology.

Understanding the benefits of renin-angiotensin-aldosterone system inhibitors and influence of hyperkalaemia on their prescription: a UK survey of healthcare professionals' views and practice

Abstract Background Renin–angiotensin–aldosterone system inhibitors (RAASi) improve outcomes in heart failure with reduced ejection fraction (HFrEF) and they should be increased to the maximally tolerated dose. However, their use is often hindered by hyperkalaemia leading to discontinuation or down titration. Purpose Assess perceptions of primary and secondary healthcare professionals regarding benefits of RAASi according to indication and, in the context of developing hyperkalaemia, to identify barriers to optimal utilisation, and to explore options for improving management protocols, specifically in HFrEF. Methods An electronic survey was distributed in primary and secondary care setting in Hampshire, UK, to evaluate knowledge about benefits of RAASi, clinical response to varying level of potassium (K+), and decision-making using several clinical scenarios. Hyperkalaemia was graded into mild (serum K+ 5.5 – 5.9 mmol/L), moderate (serum K+ 6.0 – 6.4 mmol/L) or severe (serum K+ ≥ 6.5 mmol/L). Results Between November 2021 and January 2023, 300 questionnaires were completed by 274 (91%) physicians (varying grades of seniority), 22 (7%) non-medical prescribers, and 4 (1%) pharmacists. 80% were working in secondary care across different specialities (31 working in cardiology and 15 in nephrology). The majority were aware of prognostic benefit of angiotensin converting enzyme inhibitors (ACEi) in patients with HFrEF although this was lower for mineralocorticoid receptor antagonists (MRA) (Fig 1). Fewer than 2/3 of respondents were aware of impact of ACEi on symptoms. In the presence of mild hyperkalaemia, 75% and 88% of healthcare professionals would stop or reduce ACEi and MRA, respectively. In moderate or severe hyperkalaemia, this increased to 96% and 97%, respectively (Fig 2A-B). When considering the scenario of a patient with HFrEF who had ACEi stopped due to K+ 5.9 mmol/L, the potassium at which the responder would consider re-starting the ACEi ranged from 77% when potassium level < 5.5 mmol/L to 1% in moderate or severe hyperkalaemia (Fig 2C). 74% of healthcare professionals were familiar with local hyperkalaemia management protocol and 93% supported dedicated training on RAASi dose adjustment and hyperkalaemia management. Conclusion This survey, across a broad range of healthcare professionals, shows uncertainties around knowledge of benefits of RAASi in patients with HFrEF and there is sizeable variation in the prescribing response to degrees of hyperkalaemia. Given the voluntary nature of the survey, it is plausible that these data are biased towards individuals with greater understanding. Regardless, there is a need for continued education with a goal of improving patient-centred care.