Balance Of Costs Research Articles

Reconstruction of Sparse-View X-ray Computed Tomography Based on Adaptive Total Variation Minimization.

Sparse-view reconstruction has garnered significant interest in X-ray computed tomography (CT) imaging owing to its ability to lower radiation doses and enhance detection efficiency. Among current methods for sparse-view CT reconstruction, an algorithm utilizing iterative reconstruction based on full variational regularization demonstrates good performance. The optimized direction and number of computations for the gradient operator of the regularization term play a crucial role in determining not only the reconstructed image quality but also the convergence speed of the iteration process. The conventional TV approach solely accounts for the vertical and horizontal directions of the two-dimensional plane in the gradient direction. When projection data decrease, the edges of the reconstructed image become blurred. Exploring too many gradient directions for TV terms often comes at the expense of more computational costs. To enhance the balance of computational cost and reconstruction quality, this study suggests a novel TV computation model that is founded on a four-direction gradient operator. In addition, selecting appropriate iteration parameters significantly impacts the quality of the reconstructed image. We propose a nonparametric control method utilizing the improved TV approach as a solution to the tedious manual parameter optimization issue. The relaxation parameters of projection onto convex sets (POCS) are determined according to the scanning number and numerical proportion of the projection data; according to the image error before and after iterations, the gradient descent step of the TV item is adaptively adjusted. Compared with several representative iterative reconstruction algorithms, the experimental results show that the algorithm can effectively preserve edges and suppress noise in sparse-view CT reconstruction.

Optimization waste heat recovery and power generation for industrial sustainability: A comparative study of supercritical CO2 Brayton, organic Rankine, and inverted Brayton cycles with synthesis natural gas as heat source

Generated waste heat in industrial processes, which is not applied in any functional work, is discharged into the environment by conduction, convection, and radiation from industrial products, processes, and equipment. Harnessing the waste heat carries significant attention because of its potential to decrease fossil fuel consumption and deterring of atmospheric pollutants. This study aims to provide an alternative for natural gas and reduce air pollution, serving the waste heat from the synthesis natural gas (SNG) production as the heat source for the cycles namely CO2 Brayton cycle, organic Rankine cycle, and Inverted Brayton cycles-organic Rankine cycle to the calculation of generated power, efficiencies, and total product unit cost of distinct layouts. By doing so, a comparison of the cost of synthetic natural gas between the two states can be made. Power output is analyzed concerning temperature and pressure, while exergy destruction calculations highlight the synthesis natural gas production process as the major contributor. Cost balance equations are applied to compute the cost of synthesized natural gas and total product unit cost. Objective functions encompass net power output, first law efficiency, and total product unit cost. Optimization employs the genetic algorithm method in MATLAB software. Results reveal that supercritical CO2 Brayton cycle achieves 17,100 kW power output, 0.8894 first law efficiency, and 26.32 $GJ total product unit cost. Conversely, with the inverted Brayton cycle-organic Rankine cycle, the outcomes are slightly lower, yielding 15,430 kW power output, 0.879 first law efficiency, and 25.41 $GJ total product unit cost.

Phase-field electrochemical simulations of reconstructed graphite electrodes

Graphite currently serves as the most predominant anode material in lithium-ion batteries due to its favorable balance of energy density, material cost, and relative safety in operations. While graphite anodes are manufactured mainly by randomly packing graphite particles, there is an avenue to improve the anode performance through optimized electrode microstructure. This is especially crucial for fast charging/discharging operations, where lithium plating is exaggerated and poses a critical safety concern. Therefore, the capability to investigate detailed electrochemical processes in electrode microstructures is essential for graphite anode designs. In this work, we incorporate the Cahn–Hilliard phase-field equation into a smoothed boundary method electrochemical simulation framework to account for phase transformations in graphite electrode complex microstructures upon lithiation/delithiation. These simulations quantitatively demonstrate that (i) modeling phase-separating graphite as a Li solid-solution material will overestimate graphite electrodes’ performance, (ii) particle size determines the electrode performance, (iii) pore tortuosity is important to enhance electrodes’ performance noticeably for thick electrodes, and (iv) introducing tunnels can spread insertion reaction more uniformly throughout the electrodes. Moreover, a substantial portion of this work focuses on simulating the behavior of thick electrodes. Thick electrode design holds a promising opportunity to increase overall energy density and our model enables computational predictions for such electrodes in an easy manner without conducting physical experiments. Thus, the presented simulation framework offers a fast and robust tool for designing electrode microstructures and operation protocols on digitally represented complex electrode microstructures. This approach has the potential to advance the electrode design of lithium-ion batteries.

Maximising the informativeness of new records in spatial sampling design

Abstract In building a robust knowledge base or validating existing models for use in ecological spatial modelling, having plentiful high‐quality data is paramount. Careful survey design helps attain that goal and, in part due to financial constraints, such design requires the balancing of hard monetary costs and the intangible benefit of improved ecological models. We propose a framework that quantifies a location's value to the modeller by accounting for both the probability of obtaining new samples and their expected contribution to the model. The approach is illustrated on a citizen science database of roadkills in Taiwan, modelled as a Poisson point process on a linear road network. Our method has revealed some valuable locations that were not self‐evident, for example, highlighting the possibility of sending volunteers to mountainous areas that despite being hard to reach, would provide valuable samples. We have also highlighted some ex situ sampling opportunities to avoid wasting resources by over‐sampling hard to access locations. Our technique is not restricted to presence‐only data, and in fact we present a general framework that can be applied to a wide range of settings by tuning its formulation. Our method is quite flexible and allows for more elaborate value functions, enabling managers to precisely quantify varied goals within the same framework.

Fiscal Moderating Effect for Sustaining the Upstream Petroleum Field Economics and Profitability: Inventory and Purchasing Performance

Introduction: The study literature's obvious limits with a complete fiscal term model for moderating variables are crucial to our investigation. In order to preserve petroleum field economics and profitability in Indonesia's petroleum business context, this study examines the relationship between material inventory performance, purchasing performance, and fiscal term moderating influence with an agglomeration approach. Theoretical Framework: Industry character strongly influences the inventory and purchasing performance relationship (Basu & Wang, 2011); Hofer et al., 2012); (Priniotakis & Argyropoulos, 2018). Despite circumstances and fiscal terms that were not adaptive to environmental dynamism (Acquah-Andoh et al., 2019), Production Sharing Contract (PSC) Contractors' upstream field economics deteriorated compared to the initial (Paramita, 2022). Financial term in fossil sector draws investors, ensuring fair economic performance and cost balance (Mardiana et al., 2019; Yun et al., 2020). Methodology: Cross-sectional time series data were collected over five years from 26 Production Sharing Contract (PSC) Contractors from Indonesia's upstream oil industry in the preliminary Plan of Development (POD) phase. Econometric analysis using multivariate structural equation modeling will test the model. Results and Discussions: The results showed that different financial performance agglomerations showed an inconsistent relationship between inventory performance, petroleum field economics, and profitability, but purchasing performance showed a positive relationship in any scheme, and fiscal terms significantly strengthened the relationship between variables. Conclusion: This study sheds light on whether fiscal term strengthening variables in the upstream petroleum company affect decision-making.

Sound propagation from the underfloor of railway vehicles to the wayside

It is important to consider the layout of underfloor equipment to make sure the train keeps low-noise under stationary condition. The arrangement of the underfloor equipment is important in view of not only the noise but also weight balance, maintainability and manufacturing cost. Therefore, the underfloor arrangement shall be decided at even the early stage for designing the vehicles. The early design includes the exterior noise prediction as one of the important specifications. In general, there are various-size equipment, ducts and pipes, kinds of sub-floor made of thin metal plates around the space of the underfloor. When the train is on ballasted track, both surfaces (ballast of the track / underfloor of the vehicle with ducts and piping) may have absorption effect but it is still unknown. This paper describes measurement results on the sound propagation from actual railway vehicle underfloor using dodecahedral speaker.

Nurses’ and midwives’ perceptions and preferences for lifelong learning

Lifelong learning is essential for personal and professional growth and enables individuals to gain new knowledge and skills that keep them in touch with the advancements in their work and open new career prospects (McGreal & Olcott, 2022). In the healthcare sector, lifelong learning is integral to workforce development. Workforce development is critical to ensure that nurses and midwives maintain knowledge of best practice for improved care outcomes and sustain appropriate levels of skill competence. Recently, the Australia government has implemented a range of initiatives to support the development of short courses and micro-credentials designed to deliver lifelong learning that will upskill the labour market and meet the needs of the healthcare workforce (Varadarajan et al., 2023). An Australian survey of 3,756 workers was undertaken by Deloitte (2020) to explore workforce attitudes toward lifelong learning. This study found that the majority of Health care and social assistance workers were relatively interested in further study (63%). The study also established that the majority of study-interested workers want flexible, bite-sized intensive learning that is linked to their jobs and industry (Deloitte, 2020). However, while lifelong learning is a requirement of nurses’ and midwives’ registration, little is reported on the motivational drivers, enablers and barriers associated with lifelong learning (Oliver, 2019). This Pecha Kucha reports on a study undertaken to better understand the motivational drivers of nurses’ and midwives’ engaging in lifelong learning and the enablers and barriers they face undertaking lifelong learning. An online survey of nurses and midwives was implemented, to glean their perspectives on motivational drivers, enablers and barriers for lifelong learning. Convenience sampling was used to identify participants who were registered nurses and midwives in Australia, Mauritius or Singapore. Participants were recruited by email invitation distributed through professional networks as well as education and industry providers in Australia, Mauritius and Singapore. The findings of this study confirmed that the motivational drivers for lifelong learning across the respondents were personal interest/development (62%), continuing professional development (62%) and career progression (51%). Participants noted their preference for undertaking lifelong learning was via a combination of face to face and online learning (56%). Their pedagogical preferences included interactive resources (56%), written materials (56%) and discussions with other participants (54%). Key enablers to success in lifelong learning was deemed to be flexibility in assessment submission (56%) and easy to use systems (52%). The barriers identified by respondents included work/life balance (52%) workload (49%) and cost (49%). Digital technology in relation to lifelong learning can significantly promote enablers and nullify perceived barriers. Lifelong learning should be designed to maximise engagement at minimal cost to the participant. This can be achieved by providing interactive resources and digital materials that are easy to use, intuitive and can be utilised when convenient to the participant. Online discussions are ideal but these need to be asynchronous to ensure flexibility and support for learner scheduling around work/other life commitments. These findings could be used by educators and learning designers to guide and inform the development of lifelong learning.

Metabolic and immune costs balance during natural acclimation of corals in fluctuating environments

Epigenetic modifications based on DNA methylation can rapidly improve the potential of corals to adapt to environmental pressures by increasing their phenotypic plasticity, a factor important for scleractinian corals to adapt to future global warming. However, the extent to which corals develop similar adaptive mechanisms and their specific adaptation processes remain unclear. Here, to reveal the regulatory mechanism by which DNA methylation improves thermal tolerance in Pocillopora damicornis under fluctuating environments, we analyzed genome-wide DNA methylation signatures in P. damicornis and compared the differences in the methylation and transcriptional responses of P. damicornis from fluctuating and stable environments using whole-genome bisulfite sequencing and nanopore-based RNA sequencingtranscriptome sequencing. We discovered low methylation levels in P. damicornis (average methylation 4.14%), with CpG accounting for 74.88%, CHH for 13.27%, and CHG for 11.85% of this methylation. However, methylation levels did not change between coral samples from the fluctuating and stable environments. The varied methylation levels in different regions of the gene revealed that the overall methylation level of the gene body was relatively high and showed a bimodal methylation pattern. Methylation occurs primarily in exons rather than introns within the gene body In P. damicornis, there was only a weak correlation between methylation and transcriptional changes at the individual gene level, and the methylation and gene expression levels generally exhibited a bell-shaped relationship, which we speculate may be due to the specificity of cnidarian species. Correlation analysis between methylation levels and the transcriptome revealed that the highest proportion of the top 20 enriched KEGG pathways was related to immunity. Additionally, P. damicornis collected from a high-temperature pool had a lower metabolic rate than those collected from a low-temperature pool. We hypothesize that the dynamic balance of energy–expenditure costs between immunity and metabolism is an important strategy for increasing P. damicornis tolerance. The fluctuating environment of high-temperature pools may increase the heat tolerance in corals by increasing their immunity and thus lowering their metabolism.

Deep reinforcement learning based medical supplies dispatching model for major infectious diseases: Case study of COVID-19

Stockpiling and scheduling plans for medical supplies represent essential preventive and control measures in major public health events. In the face of major infectious diseases, such as the novel coronavirus disease (COVID-19), the outbreak trend and variability of disease strains are often unpredictable. Hence, it is necessary to optimally adjust the prevention and control dispatching strategy according to the circumstances and outbreak locations to maintain economic development while ensuring the human health survival, however, many models in this scenario seldom consider the dynamic material prediction and the measurement of multiple costs at the same time. Taking the COVID-19 scenario as a case study, we establish a deep reinforcement learning (DRL)-based medical supplies dispatching (MSD) model for major infectious diseases, considering the volatility of the COVID-19 situation and the discrepancy between medical material demand and supply due to the high infectiousness of the Omicron series strains. The present model has three main components: 1) First, for the dynamic medical material prediction problem in complex infectious disease scenarios, taking the lifted COVID-19 lockdown scenario as an example, the modified susceptible-exposed-infected-recovered (SEIR) model was utilized to analyze the spread of the COVID-19, understand its characteristics, and map out the related medical supplies demand; 2) Second, to break away from the previous premise of only considering supply-demand, this study adds scheduling rules and cost function that weighs health and economic costs. An epidemic dispatching optimization model (Epi_DispatchOptim) was established using the OpenAI Gym toolkit to form an environment structure with virus transmission space, and emergency MSD while considering both human health and economic costs. This architecture interprets the balance between the supply-demand of medical supplies and reflects the importance of MSD in the balanced development of health and economy under the spread of infectious diseases; 3) Finally, the MSD strategy under the balance of health and economic cost is explored in Epi_DispatchOptim using reinforcement learning (RL) and the evolutionary algorithm (EA). Experiments conducted on two datasets indicate that the RL and EA reduce economic as well as health costs compared to the original environmental strategies. The above study illustrates how to use epidemiological models to predict the demand for healthcare supplies as the premise of scheduling models, and use Epi_DispatchOptim to explore the dynamic MSD decisions under mortality and economic equilibrium. In Shanghai, China, the economic cost of the exploration strategy is reduced by 27.36–27.07B compared to static scheduling, and deaths are reduced by 126–150 in 150 day compared to the no-intervention scenario. By integrating knowledge of epidemiology, optimal decision making, and economics, Epi_DispatchOptim further constructs epidemiological models, cost functions, state-action spaces, and other modules to assist public health decision makers in adopting appropriate MSD strategies for major public health event.

Multi-objective turbine allocation on a wind farm site

The Multi-Objective Turbine Allocation (MOTA) method is introduced as a novel approach for wind farm layout optimization and site selection. By incorporating Geographic Information System (GIS) tools and the Analytical Hierarchy Process (AHP), the MOTA method offers a comprehensive solution to balance energy production, cost factors, and environmental impacts. In this study, the MOTA method is applied to Gökçeada, Türkiye, for wind farm development. Results show that the MOTA method effectively proposes the optimum wind farm layout by selecting the best site for each turbine. The sequential turbine allocation approach, integration of multiple objectives, and use of GIS tools and AHP are the key capabilities and novelties of the MOTA method. The method allows for flexible investment decisions, considering technical and economic aspects. The outcomes from the Gökçeada case study highlight the effectiveness of the MOTA method in maximizing energy production while considering cost factors and environmental impacts. The results indicate that for the selected objective functions, the optimal net profit is attained with the installation of 155 turbines on Gökçeada. The MOTA method presents a practical and efficient solution for wind farm development, contributing to sustainable and efficient renewable energy generation.

The effect of stress on rates of asexual reproduction in an invasive planarian

Animal reproduction under stressful conditions is often reduced, with current survival and future reproduction being generally traded off against current reproductive activity. This study examines the impacts of physical and chemical stressors on the rates of asexual reproduction of the invasive planarian Girardia tigrina. 320 wild-caught planaria (mixed size class) were kept individually in Petri dishes such that their individual rates of fission through fragmentation could be easily monitored. Four treatment groups were compared, one chemical (5 mg/L ammonia) and one physical (decapitation), in comparison to a negative control (animals were starved of food) and a positive control where the animals were given an abundance of food. The two treatment groups immediately began reproducing asexually and accumulated the highest number of fissions over the course of the 12-day investigation period, while the positive control only began to fission after 7 days. We propose that the reproductive response observed here is an adaptive one to stressful conditions, whereby the likelihood of survival through numerical abundance is enhanced, although the size and vulnerability of resulting fragments may impose a balancing cost. The response may play a role in the invasiveness of G. tigrina by making it able to colonize environments where adverse conditions prevail.

Service failure monitoring via multivariate multiple linear regression profile schemes with dimensionality reduction

Service failures can be manifested through customer complaints in the form of negative reviews. It is vital for businesses to monitor the occurrence and severity of customer complaints to help balance service failure responses and costs. Existing monitoring schemes can detect complaint anomalies but fail to explain the reasons for these anomalies. We propose new multivariate multiple linear regression profile (MMLRP) based schemes to monitor the relationship between customer complaints and identified explanatory variables. Conventional MMLRP-based schemes suffer performance loss with more than five explanatory variables, and the fixed parameters drawn from a historical process are unsuitable for a changing review-generation process. As such, we apply dimensionality reduction techniques to the explanatory variables and incorporate the variability in new review samples. We use both simulation analysis and an airline service case study to show the effectiveness of our proposed MMLRP-based schemes in customer complaint anomaly detection and diagnosis.

Analisis Pengendalian Persediaan Barang Menggunakan Metode EOQ

The aim of this research, based on the problem formulation above, is to find out that inventory management by using the Ecolnolmic Order Quantity (EOQ) met mode can minimize the total inventory costs UMKM Home Industry Rumah Tempe Asli HB Pematangsiantar. This thesis research uses a quantitative descriptive method. The method used in controlling tempe goods inventory is the EOQ method. The EOQ method is a inventory management technique carried out by the company by determining the most efficient inventory amount of goods and producing balanced processing costs and storage costs, so that The company can minimize inventory costs. From the results of research using the EOQ method, the total inventory carried out with the EOQ metdown is 80.179 pcs with a purchase frequency of 22 times. The amount of security stock required by UMKM Home Industry Rumah Tempe Asli HB Pematangsiantar is 8.200 pcs. Total inventory using the EOQ method is IDR Rp1.122.421. EOQ meting can reduce the costs of providing merchandise more or can be said to be efficient if applied to UMKM Home Industry Rumah Tempe Asli HB Pematangsiantar. In other words, the inventory control carried out by the company is still not yet optimal. By using the EOlQ met mode in managing mobile inventory, Rumah Tempe Asli HB can optimize the cost of mobile inventory.

Leveraging flexibility of residential heat pumps through local energy markets

The integration of variable renewable energy sources such as wind and solar energy has made demand-side flexibility a critical aspect for balancing the power grid during fluctuating power generation. In recent years, heat pumps have gained increasing attention for their flexibility potential. While demand response programs have been extensively discussed to leverage flexibility, the market-based approach of local energy markets (LEMs) requires more attention. LEMs provide a marketplace for local energy exchange, facilitating a more balanced energy system by harnessing the flexibility of heat pumps at lower costs. Therefore, this study examines the economic benefits of leveraging flexibility of heat pumps through LEMs and the problems that may arise. An agent-based simulation of LEMs with double-sided auctions is utilized to consider prosumer behavior, incorporate model-predictive control, and employ detailed modeling of energy devices. According to the findings, a district where 40% of households use heat pumps can reduce their annual cost by 5.1% through a LEM. The study identifies several factors contributing to the relatively small economic benefits, including high balancing costs, excessive taxes and network charges, uneven distribution of benefits, and seasonal fluctuations. Additionally, the study proposes daily demand charges to mitigate high residual demand peaks resulting from heat pumps. In conclusion, the study emphasizes the key role of heat pumps in achieving economic benefits through LEMs and highlights the regulatory framework changes required to effectively tackle the challenges faced by LEMs with a large share of heat pumps.

Fe-based high-entropy alloy with excellent mechanical properties enabled by nanosized precipitates and heterogeneous grain distribution

High-entropy alloys (HEAs) consisting of CoCrFeNiAlTi systems, with a face-centered cubic (FCC) matrix reinforced by ordered L12 precipitates, have demonstrated exceptional strength-ductility combinations. However, the current compositional design of HEAs heavily relies on high Ni and Co contents, compromising the balance between properties and cost. Thus, it is crucial to optimize the cost-performance trade-off by fine-tuning the range of Fe, Co, and Ni, while maintaining excellent strength-ductility combination. In this study, we propose a novel Fe-based HEA with nanosized precipitates and a heterogeneous grain distribution, achieving a strength-ductility combination comparable to state-of-the-art Ni- or Co-based HEAs. The alloy benefits from both precipitation hardening and hetero-deformation-induced strengthening attributed to the heterogeneous grain distribution, resulting in excellent yield strength of 1433 MPa, tensile strength of 1599 MPa, and ductility of 22%. The microstructural evolution and its influence on mechanical properties are unraveled with respect to the observation of precipitate-dislocation interaction and hetero-deformation-induced stress (HDI stress) evaluation. This study suggests that the challenge of balancing properties and cost can be addressed through optimized compositional and microstructural design.

Second-Line Therapy Sequencing in Primary Warm Autoimmune Hemolytic Anemia: Splenectomy-Rituximab Versus Rituximab-Splenectomy

Introduction: Primary warm autoimmune hemolytic anemia (wAIHA) is a hematologic condition that affects approximately 1 in 8000 people and occurs when IgG antibodies directed against self-erythrocytes cause hemolysis and exceed the bone marrow's ability to compensate. First-line treatment with glucocorticoids yields sustained remission in less than 30% of individuals. For patients requiring second-line treatment, splenectomy was the preferred treatment for over 50 years, being recommended over rituximab by leading experts as recently as 2010. In the last decade this practice shifted due to concerns of tripartite risk of splenectomy with rates of infection, thrombosis, and perioperative mortality, juxtaposed against the increased experience with rituximab. These risks are outdated as they predate the modern-day vaccination, thromboprophylaxis, and laparoscopic splenectomy era, respectively. Summarily, the 2017 British guidelines dropped splenectomy from second- to third-line for patients with primary wAIHA, consistent with expert opinion in the United States in 2023. Over these same twenty years, the mortality and morbidity of splenectomy has greatly decreased, owing to updated preoperative vaccination standards, prevention of thrombosis, and widespread use of the laparoscopic (i.e., rather than open) technique. Despite these advancements, splenectomy is now used in fewer than 10% of patients with wAIHA. In the absence of a clinical trial randomizing patients to splenectomy versus rituximab, we conducted the first simulation of second-line therapeutic sequencing for adult patients with primary wAIHA with a focus on short-run and long-run effectiveness results. Methods: We constructed a Markov simulation of adult patients with primary wAIHA and requiring second-line therapy to compare the effectiveness of strategy #1: splenectomy followed by rituximab, if necessary, versus strategy #2: rituximab followed by splenectomy, if necessary. Transition probabilities for initial response and time-variant relapse risk with rituximab and with splenectomy were drawn from retrospective primary-wAIHA-specific literature. For rituximab, we assumed that 1) patients maintained in remission for three years following treatment with a rituximab cycle would remain in remission and 2) all patients who relapse more than a year after receiving rituximab, would be retreated with rituximab. Costs were sourced from the Centers for Medicare & Medicaid Services and the Centers for Disease Control and Prevention. Utility values for patients with wAIHA were specific to the reported patient disease status, whether in relapse, partial remission (defined as hemoglobin >10g/dL) or complete remission (defined as hemoglobin >12g/dL). These were sourced from the Global Burden of Disease Study and weighted for the degree of anemia corresponding with each status. Effectiveness was calculated in quality-adjusted life-years (QALYs). The primary outcomes were lifetime discounted costs, QALYs, and net monetary benefits across accepted willingness-to-pay thresholds over short- and long-runs at 5 years and lifetime, respectively. Probabilistic sensitivity analysis was employed to vary all parameters across their uncertainty intervals simultaneously over 10,000 Monte Carlo iterations. Results: In the short-run, strategies #1 and #2 cost $39,700 and $47,100 and accrued 5.21 and 5.26 QALYs (Table 1). In the long run, costs were $909,000 and $912,000 while accruing 22.0 and 22.1 QALYs. Net monetary benefit point estimates were similar across the accepted range of WTP thresholds in both the short- and long-runs. Conclusion: The balance of costs and quality-adjusted life expectancy appears to be comparatively tightly balanced in the sequencing of second-line therapies in primary wAIHA with splenectomy-rituximab versus rituximab-splenectomy. The absence of a clear rituximab-early advantage on a population level may be driven by advancements in vaccination, thromboprophylaxis, and laparoscopic surgical techniques for splenectomy over the last 20 years. Value-of-information analysis would help identify parameters whose uncertainty reduction with future study in the real world would yield a treatment strategy with a definitive advantage. In the meantime, patients whose values and preferences align with splenectomy should not be dissuaded from pursuing it due to perceived risk.

BQL-DRS: A Novel Balanced Q-Learning Based Demand Response System for IoT based Smart Grids

The modernization of electricity networks and the integration of renewable energy resources in Internet of Things (IoT) based smart grids have led to increased variability in market prices, necessitating effective demand response (DR) strategies. To address this challenge, this paper proposes a novel Balanced Q-Learning based Demand Response System (BQL-DRS) that combines both optimistic and pessimistic targets in the Q-learning algorithm to achieve a balanced decision-making process in IoT based smart grids. It optimizes DR actions by efficiently managing consumer demand in real-time, considering IoT data from grid conditions, energy prices, and consumer preferences. The significance of the BQL-DRS lies in its ability to handle dynamic and uncertain IoT based grid environments, enabling it to make informed and cautious decisions while pursuing energy efficiency and cost-effectiveness. By effectively addressing both pessimistic and optimistic scenarios, the BQL-DRS ensures grid stability, load balancing, and substantial cost savings compared to representative models.

Design and spatial pattern optimization for a sponge city using factor analysis and geographical statistics to restore urban resilience: A case study in a coastal area of China

Abstract The sponge city is a new concept of stormwater management for ecological city construction, which aims to restore water-cycle processes and reduce runoff. Cities in coastal districts are suffering from serious instability due to high population density, urbanization, and land-use changes. However, previous research contains few evaluations of balancing urban ecological indicators of sponge city performance, including geographical, environmental, economic, and social factors, and their effect on resilience at a macro level to develop low-impact development schemes. In this study, we developed an integrated framework using factor analysis, geographical statistics, multi-objective analysis, and remote sensing methods to extract the factors influencing sponge city resilience and to establish spatial pattern schemes. The results indicated that the urbanization degree and plant adaptability had the greatest impact on sponge city performance, with weights of 45 and 27%, respectively. Sponge city spatial pattern schemes performed the best in the combination scenario of 14.8–46.8% green roofs (by area ratio) supported by grooves and rain barrels +10% herbaceous basins divided into units by ecological tree pools +10% permeable pavements and sidewalks. This scenario balanced facilities and cost to optimize the spatial pattern, which improved sponge city adaptability and urban ecological conditions.

Optimization of alkaline electrolyzer operation in renewable energy power systems: A universal modeling approach for enhanced hydrogen production efficiency and cost-effectiveness

This study presents a comprehensive analysis of an Alkaline Water Electrolysis System (AWES) for hydrogen production, focusing on thermal balance, operating conditions, efficiency, and lifecycle cost. A thermal balance model was developed and further refined using machine learning techniques based on experimental data, to accurately calculate electrolysis power, cooling requirements, and temperature maintenance, enabling cost-effective operation. This study provides insights for optimizing AWES performance and economic feasibility in hydrogen production. Selecting appropriate operating conditions considering current density, inlet temperature, and electricity price is crucial. Implementing these optimizations improves energy efficiency, reduces lifecycle hydrogen production costs, and enables integration with renewable energy systems.

The Role of Organizational Culture in Mediation Dimensions of Good Corporate Governance in The Era of National Health Assurance on Performance the Blud of Hm Djafar Harun Hospital Kolaka Utara District

This study aims to look at the role of organizational culture in mediating the dimensions of GCG in the era of national health insurance on performance at the BLUD HM Djafar Harun Hospital, North Kolaka Regency, using quantitative research. The population of this study was civil servants and non-PNS employees who worked at the BLUD of the HM Djafar Harun Hospital, North Kolaka Regency, Southeast Sulawesi Province, namely 286 people who were selected based on Presidential Decree No. 77 of 2015. A Result the transparency dimension is formed by seven indicators and patient rights is the strongest indicator in forming the transparency dimension. In the accountability dimension, there are four indicators and the one that contributes the most to this dimension is the responsibility indicator. The dimension of responsibility has three indicators and the strongest in shaping this dimension are the indicators of obligation, ethics and rational decisions. The independence dimension is formed by five indicators and the strongest form of independence is the indicator of not having personal interests. In the fairness dimension, there are five indicators and the strongest form of this dimension is the cost balance indicator. Organizational culture in this study is composed of three indicators and the strongest in shaping this dimension are innovative and supportive. The performance in this study is composed of seven indicators, where the claim return indicator gives the greatest contribution to this variable. Good Corporate Governance based on the transparency dimension has an indirect effect on hospital performance in the JKN era. Good Corporate Governance based on the dimension of accountability has an indirect effect on hospital performance in the JKN era. Good Corporate Governance based on the dimension of responsibility besides having a direct influence is also a dimension that strengthens organizational culture on hospital performance in the JKN era. Good Corporate Governance based on the independence dimension has a direct effect on hospital performance in the JKN era. Good Corporate Governance based on the dimension of fairness has an indirect effect on hospital performance in the JKN era. Organizational culture directly has a significant effect on hospital performance.