System Cost Research Articles

An on-demand collaborative edge caching strategy for edge–fog–cloud environment

In this paper, we tackle the critical challenges of content edge caching, such as limited storage capacity, content popularity prediction, dynamic user demand, and user privacy, issues that most existing studies only address partially. We present an innovative Genetic Algorithm-based On-demand Collaborative Edge Caching mechanism (GAOCEC), which introduces a multi-tiered caching architecture integrating cloud, fog, and edge computing. To enhance caching efficiency and minimize system cost, a novel on-demand caching quota mechanism is proposed that dynamically allocates cache resources to edge servers. To strengthen user privacy protection during content popularity prediction, a CNN-BiLSTM-based Federated Learning algorithm (CBFL) is presented that ensures high prediction accuracy without the need to upload local data to the cloud. We also refine the genetic algorithm for content placement by fine-tuning various parameter sets to identify the optimal balance between latency reduction and caching cost. Our experimental results validate the effectiveness of our approach, demonstrating increased cache hit rates, decreased content response times, and an overall improvement in system efficiency. This work provides a comprehensive, adaptive, and privacy-preserving solution for the edge–fog–cloud environment.

Energy Consumption in Wheat-Rice Cropping System in Punjab

Energy consumption for crop production depends on the availability of energy sources and the capacity of the farmers. This study analyzed energy sources used by farmers for wheat and rice production and identified agricultural operations where energy consumption can be reduced so that operational costs in wheat-rice production system can be lowered. Data on energy use in wheat and rice cropping system was collected by interviewing the farmers using specially designed questionnaire. Relevant information pertaining to human labour use, sources of irrigation, number of irrigations to each crop, quantity of farm inputs, utilization of mechanical power sources and grain yield etc. were collected. The average output energy of grain, input energy, energy ratio and specific energy for wheat crop in the state of Punjab were 68347 MJ ha-1, 15644 MJ ha-1, 8.4 and 3.08 MJ kg-1, respectively. The average output energy of grain, input energy, energy ratio and specific energy for rice were 115888 MJ ha-1, 16438 MJ ha-1, 14.8 and 2.09 MJ kg-1, respectively. For wheat-rice cropping system, the average output energy of grain, input energy, energy ratio and specific energy were 184234.4 MJ ha-1, 32081.4 MJ ha-1, 11.7 and 2.48 MJ kg-1, respectively. Energy ratio (output energy to input energy ratio) of rice is higher than the wheat, indicating rice efficiently utilized energy resources to convert into output of the harvested crop. Similarly, lower specific energy (input energy to yield ratio) of rice as compared to wheat also indicates lower energy consumption to produce per unit of rice. This analysis clearly indicates that rice production is more energy efficient than wheat production in rice-wheat production system. Further, the energy ratio, specific energy and energy productivity of wheat-rice cropping system were estimated as 11.6 to 11.9, 2.44 to 2.51 MJ kg-1 and 0.40 to 0.41kg MJ-1, respectively. This study will be helpful to identify the agricultural operations and implementing suitable interventions for improving energy use efficiency.

B-116 Cost Estimation of Laboratory Test Overuse in 14 Ordered Parameters: Experience From 6 hospitals and 65 Primary Care Centers

Abstract Background Inappropriate retesting and test request are considered causes of laboratory overutilization. The former can be managed through minimum retesting intervals (MRI) and the latter with expert rules in the laboratory information system (LIS). The aim of this study was to assess the effectiveness of both tools. Methods We performed a retrospective analysis of 14 laboratory routine tests subject to MRI or expert rules among 6 hospitals and 65 primary care centers from March 2018 through December 2023. Both tools were gradually implemented in the LIS (Servolab4®, Siemens, Berlin, Germany). Data from an export software was used for analysis (Health Analytics, Siemens Healthineers, Madrid, Spain). Results During the observation period, a total of 493.640 tests requests were analyzed and 60.151 violations were counted (Table 1). All violations represented an economic saving of 494.603€ (8.760€ per month). The most popular rejected test was 1,25-DihydroxyvitaminD (34,5%), followed by thyroid peroxidase antibodies (20,3%) and 25-HydroxyVitaminD (19%). These three tests were responsible for 73,8% of the total cost savings. Conclusions The real cost estimation of inappropriateness is difficult to assess, but the generated costs for the health system are certainly high and not negligible. Moreover, addressing overutilization would ensure standardization and high-quality care.

Multi-objective energy management using a smart charging technique of a microgrid with the charging impact of plug-in hybrid electric vehicles

The Microgrid (MG) concept is being developed to better integrate renewable energy sources and automate distribution networks. Microgrids combine distributed generating units (DGs) and energy storage systems to achieve this. This research paper aims to simultaneously minimize the daily operational cost and net environmental pollution of a small MG system, factoring in the charging demand from Plug-in-Hybrid Electric Vehicles (PHEVs) and consumer load demands. The proposed energy management process not only minimizes operational costs and emissions, but also determines the optimal battery size for the energy storage system. The analysis also explores the importance of two critical variables - the operation and maintenance costs of the DGs, and the total daily cost of the battery energy storage system. The demand for PHEV charging is managed using an intelligent charging approach. Given the complexity of the optimization, a recently developed metaheuristic algorithm, Slime Mould Algorithm (SMA), is applied. The performance of SMA is compared against the Grasshopper Optimization Algorithm and Sine Cosine Algorithm. To solve the multi-objective problem, a weighted sum method maintaining non-dominance and a fuzzy decision-maker technique are employed alongside the suggested algorithms. Three different scenarios verify the proposed method's effectiveness.

Protocol for osteoporosis screening in CPTS

Osteoporosis is a chronic disease that affects bone density and quality, making the skeleton more fragile and susceptible to fracture in the event of minor trauma, or even spontaneously in the most severe cases. It seriously affects patients' quality of life and generates considerable costs for healthcare systems. Given this situation, early detection and management of this disease is crucial. With this in mind, the Communauté Professionnelle Territoriale de Santé (CPTS) in southern Toulouse has developed a care pathway based on a cooperation protocol designed to facilitate screening for the disease.

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

Large-scale access to distributed energy resources leads to new energy consumption problems and safe operation risks in the power system. Virtual power plants and shared energy storage are effective ways to promote the flexible consumption of distributed energy resources and improve the reliability and economy of power system operation. Based on the concept of sharing economy and considering the complementary characteristics of source and load resources between different virtual power plants, this paper focuses on the optimisation of shared energy storage and multi-virtual power plant operation. Firstly, distributed wind power, distributed photovoltaic and flexible load resources are aggregated into virtual power plants to analyze the cooperative operation mode of shared energy storage and multi-virtual power plant systems. Secondly, a two-layer decision model for shared energy storage configuration and multi-VPP system operation optimisation is constructed, with the upper model solving the optimal energy storage configuration scheme by maximising the revenue of the shared energy storage operator, and the lower model optimising the multi-VPP system operation strategy by minimising the total operation cost, the maximum amount of new energy consumed, and the minimum amount of carbon emission as the multi-objective optimisation strategy. Finally, a simulation analysis is carried out, and the results show that compared with the independent operation mode of each virtual power plant, the model proposed in this paper increases the annual profit of the shared energy storage operator by 7180¥, reduces the operating cost of the VPP system by 7.08 %, improves the rate of renewable energy consumption by 0.82 %, and reduces the annual carbon emission by 54.91 t, which realizes the mutual benefits of the virtual power plant and the shared energy storage.

Towards decarbonizing large-scale industries: A decision support framework for optimizing grid-connected PV-battery energy systems planning – Case study of an OCP mining site, Morocco, North Africa

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration.

Reducing Intermittency Using Distributed Wind Energy: Are Wind Patterns Sufficiently Diversified Within France?

Wind energy (WE) is a volatile source of electricity production. In France and worldwide, the development of renewable energy sources (RES) is increasing the cost of the energy system; these will increase further to reach the WE target of 33.2 GW of installed capacity by 2028. Moreover, intermittency decreases as the installed capacity is geographically dispersed, hence the importance of investigating distributed wind deployment strategies in complementary locations. However, identifying potential wind energy locations that provide complementarity is challenging, especially given the inherent chaotic nature of wind during time. The objective of this research is to propose an adequate methodology to cluster wind time series (TS) to provide insights on smart planning considering distributed wind energy (WE) production. Results reveal that Shape Based Distance (SBD) classifiers perform best in clustering TS and appear relevant in identifying potential wind complementary locations in France. Ultimately, intermittency measures show that, in the case of complementary wind locations, availability (AVA) can increase by about 31% and the variability can decrease by about 30%.

Standoff Identification of Plastic Waste Using a Low-Cost Compact Laser-Induced Breakdown Spectroscopy (LIBS) Detection System.

We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m. A compact non-gated Czerny-Turner charge-coupled device (CCD) spectrometer (CT-CCD) is utilized to analyze the optical response. The single lens and CT-CCD combination not only reduces the cost of the detection system by tenfold, but also decreases the collection system size and weight compared to heavy telescopic-based intensified CCD systems. All the samples investigated in this study were collected from a local recycling plant. All the measurements were performed with only a single laser shot which enables rapid identification while probing a large number of samples in real time. Furthermore, principal component analysis has shown excellent separation among the samples and an artificial neural network analysis has revealed that plastic waste can be identified within ∼10 ms only (testing time) with accuracies up to ∼99%. Finally, these results have the potential to build a compact and low-cost ST-LIBS detection system for the rapid identification of plastic waste for real-time waste management applications.

Performance investigation of a high-temperature absorption-compression heat transformer with liquid refrigerant injection

The high-temperature heat pump technology is a promising approach to utilizing low-temperature waste heat while meeting industrial demands for high-temperature heat. However, existing high-temperature heat pump systems face challenges including insufficient temperature lift, system complexity, and rapid performance degradation at high output temperature. This paper proposes a novel, structurally simple high-temperature absorption-compression heat transformer, aiming to recover heat below 100 °C and achieve a large temperature lift above 70 °C. The system is analyzed from the aspects of energy, exergy, key parameters that impact its performance, and techno-economic comparison. Results show that injecting liquid refrigerant into the compressor chamber mitigates high superheat effects on the compressor, allowing for higher absorption pressure. The system achieves an output temperature of around 170 °C, with the compressor outlet temperature below 120 °C. Moreover, some liquid refrigerant evaporates by absorbing compressor superheat. This reduces power consumption of the compressor, heat input of the evaporator, and overall system exergy loss. Thus, the proposed system demonstrates improvements of 6.5 % and 7.5 % in coefficient of performance and exergy coefficient of performance, respectively. Besides, it is feasible to achieve absorption temperatures exceeding 170 °C by increasing the solution concentration or the pressure ratio, with the former being the more cost-effective approach. Furthermore, the specific costs of the proposed system are more than 20 % lower than those of other types of high-temperature heat pumps, demonstrating its superior economic feasibility.

National health and economic impact of a lifestyle program to prevent type 2 diabetes mellitus in Germany: a simulation study

IntroductionTo examine the long-term health and economic impact of a lifestyle diabetes prevention program in people with high risk of developing type 2 diabetes in Germany.Research design and methodsWe assessed...

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.

Vehicle platoon safety considering the vehicle cut-in: A coupled reinforcement learning and model predictive control approach

A longitudinal platoon control method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) and Model Predictive Control (MPC) is proposed to solve the problems of low following efficiency and system instability in longitudinal platoon control. Firstly, Dynamic Bayesian Network (DBN) and Long Short-Term Memory (LSTM) network are introduced to identify the driving behavior of bystanders and derive the MPC objective constraint function containing three indicators of following, comfort and fuel consumption according to the platoon dynamics equation. Secondly, the system prediction model and cost function are introduced into the action-critic network of TD3 to solve the problem of no model training in the traditional TD3 algorithm and to speed up the training speed and accuracy of the network. On this basis, a Bellman equation is proposed to calculate the time-domain difference error and the expected loss function to solve the TD3 network overestimation problem. Finally, the joint simulation platform is built to simulate the platoon driving conditions and compare with the DDPG optimization algorithm and the traditional MPC algorithm respectively. The results show that the improved TD3-MPC algorithm satisfies the constraints and the spacing error is controlled within 0.3 m, and the vehicle speed changes more smoothly in the scenario of speed fluctuation in the front vehicle, and the ride comfort of the platoon is improved, and it has better robustness in the scenario of vehicle cut-in. The experimental results show that when the vehicle speeds are 20, 40, and 60 km/h, compared to MPC, the average spacing errors are reduced by 27.56%, 25.64%, and 28.04%, respectively, and compared to DDPG-MPC, the average spacing errors are reduced by 6.59%, 8.77%, and 7.86%, respectively.

INTEGRATING SENSOR NETWORKS TO FACILITATE EFFICIENT ENERGY MANAGEMENT FOR SMART GRIDS

Organizations are implementing Smart Energy Management Systems (SEMS) to regulate energy consumption patterns and respond to energy-saving instructions due to rising energy prices and demand. The study proposes an intelligent energy management solution that integrates an IoT middleware module and Energy Controller for effective demand side regulation. The study offers an intelligent energy management solution for smart settings that integrates an IoT middleware module and Energy Controller to regulate the demand side effectively. Every IoT device has an energy controller connected, incorporating sensors and actuators. The proposed solution optimizes air conditioner energy usage in Pakistan by analyzing exterior temperature conditions, building dynamics, and data from smart devices. It uses functional interfaces, robust algorithms, and continuous tracking for assessment. To evaluate the proposed methodology the research highlights the system's key benefits and reveals significant energy savings. The smart energy management system provides better air conditioning system control, real-time monitoring, cost savings, environmental advantages, and longer equipment life.

Mitigating Risk of Acute Kidney Injury Among Children With Methicillin-resistant Staphylococcus aureus Osteomyelitis.

Children with acute hematogenous osteomyelitis (AHO) from methicillin-resistant Staphylococcus aureus (MRSA) are treated with vancomycin despite the risk of acute kidney injury (AKI). This study evaluates the rate of AKI and resource utilization for children with or without AKI when vancomycin is used in this setting. Children with MRSA AHO treated with vancomycin were retrospectively studied. AKI was assessed by clinical diagnosis and Kidney Disease Improving Global Outcomes (KDIGO) criteria. Cohorts of children with or without AKI were compared for differences in treatment, resource utilization, and outcomes. Multivariate logistic regression analysis assessed factors associated with risk for AKI. Cost analysis was performed using the Pediatric Health Information System and Healthcare Cost and Utilization Project databases. Among 85 children studied, 14 (16.5%) had chart-diagnosed AKI and 24 (28.2%) met KDIGO criteria. Children with AKI had more febrile days and higher thrombosis rates. They had longer vancomycin treatment (8 vs 5d), higher troughs (27.8 vs 17.5mg/L), and prolonged hospitalization (19.9 vs 11.1d). Multivariate analysis found a maximum vancomycin trough level (odds ratio: 1.05, P = 0.003) with a cutoff of 21.7mg/L predicted AKI.Only 2 of 20 (10%) children who had MRSA isolates with a minimum inhibitory concentration of 2 achieved therapeutic vancomycin levels. Pediatric Health Information System data of 3133 children with AHO treated with vancomycin identified 75 (2.4%) with AKI who had significantly longer lengths of stay (13 vs 7d) and higher billed charges ($117K vs $51K) than children without AKI. Chart documentation of AKI (16.5%) grossly underestimated KDIGO-defined occurrence (28.2%). This study showed that vancomycin-associated AKI required substantially greater resource utilization and higher health care costs. Lowering the targeted trough range, shortening the duration of vancomycin therapy, and considering alternative antibiotics when minimum inhibitory concentration ≥2 will reduce the risk and cost of AKI among children with MRSA AHO. Level III-retrospective comparative therapeutic study.

Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using Levy Flight Algorithm

The study addresses the integration of hybrid hydrogen (H2) and battery (BT) energy storage systems into a renewable energy microgrid comprising solar photovoltaic (PV) and wind turbine (WT) systems. The research problem focuses on improving the effectiveness and computational efficiency of energy management systems (EMS) while ensuring high system reliability. Despite the existing optimization methods for hybrid microgrids, challenges remain in optimizing energy storage and capacity planning in grid-connected microgrids. To solve this, we propose the use of the Levy Flight Algorithm (LFA) to optimize the capacities of PV, WT, H2 tanks, electrolyzers (EL), fuel cells (FC), and BT, which presents a complex nonlinear optimization challenge. The novelty of this study lies in integrating the LFA with a rule-based EMS, enhancing system reliability and efficiency. The proposed approach significantly reduces the annualized system cost (ASC) and the levelized cost of energy (LCOE). The result demonstrate that the LFA outperforms methods like the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), yielding cost savings of $3,309, $5,297, $4,484, and $5,129 respectively. The LFA achieves the lowest LCOE at $0.275/kWh, compared to $0.278/kWh with SSA, $0.289/kWh with GA, $0.280/kWh with PSO and $0.283/kWh with GWO. This research contributes to the broader scientific community by providing a more efficient approach to optimizing renewable energy microgrids with hybrid storage systems, thus promoting eco-friendly and cost-effective energy solutions. The proposed system design offers a pathway to future energy systems with high renewable integration, especially as technology advances and costs continue to decrease.

A hybrid renewable energy system for Hassi Messaoud region of Algeria: modeling and optimal sizing

The growing global energy demand and the need to mitigate greenhouse gas emissions have driven the exploration of sustainable and efficient energy solutions. In Algeria, where the energy sector relies heavily on fossil fuels, integrating renewable energy systems is essential for enhancing energy security and reducing environmental impacts. This study focuses on optimizing a hybrid renewable energy system (HRES) for off-grid applications in the Hassi Messaoud region of Algeria to balance technical performance, economic viability, and environmental sustainability.A hybrid system consisting of photovoltaic (PV) panels, wind turbines (WT), fuel cells (FC), and diesel generators (DG) was modeled and optimized using a genetic algorithm (GA). The optimization process aims to minimize the annual cost of the system while ensuring high reliability, as measured by the loss of power supply probability (LPSP), and maximizing the use of renewable energy. A particle swarm optimization (PSO) approach was also implemented for comparison, highlighting the advantages of the GA in terms of cost distribution and system reliability. The optimized HRES demonstrated that renewable sources (PV and WT) provided 77% of the total energy demand, with an overall system cost of 0.18080$×kWh−1, significantly lower than recent studies, which reported costs between $0.213 and 0.609$×kWh−1. FCs contributed 14% to the load, whereas DGs were limited to 8% to minimize emissions, resulting in annual CO2 emissions of 10,865 kg and a relative emission rate of 3.608 gCO2eq×kWh−1. Economic analysis showed that DGs and FCs accounted for 44% and 24% of the annual cost, respectively, highlighting the impact of backup systems in ensuring reliability.Sensitivity analysis under varying load demands and renewable energy availability confirmed the robustness of the system, and the GA approach was found to be more effective than PSO in maintaining cost efficiency and reliability. Additionally, the social analysis highlighted a renewable fraction (RF) of 91.5%, emphasizing the contribution of the system to sustainable energy practices. These findings validate GA-based optimization as a superior method for designing cost-effective, reliable, and environmentally sustainable HRES, offering significant potential to reduce fossil fuel dependency in industrial applications. These results not only support the broader adoption of renewable energy systems in similar regions but also contribute valuable insights for future research and policy development in the field of energy sustainability.

Assessment of climate change mitigation potential of the Kosovo energy and transport sector

The Energy and transport sector in Kosovo are the main emitter of greenhouse gases (GHG) with a share of about 85% in total annual emissions. In energetic sector, 95-97% of emissions are associated with the electricity generation due to the predominant role of lignite fuelled power plants. The electricity sector contributes about 80% of total CO2 emissions in Kosovo. Those make the energy and transport sector the main emitters of CO2 and also the main target for CO2 emissions reduction. The National Energy and Climate Plan 2021-2030 aims to ensure a reliable supply of energy for Kosovo and proposes targets for renewable, energy efficiency, and greenhouse gas reductions. This paper presents analyses of different time frame scenarios for development of the Kosovo energy system in relation to climate change mitigation potential of the Kosovo energy and transport sector. The national energy system of Kosovo is modelled using EnergyPLAN software which integrates energy for electricity, transport and heat, and includes hourly fluctuations of energy demand and energy production. Three scenarios of Kosovo Energy system are modelled; starting point scenario 2019 and two long term scenarios 2030 with 23.3% and 41.8% share of renewable. An analysis of the overall energy system costs and security of supply with a main focus in electricity sector of the three scenarios is performed.

Optimal triage of patients with acute chest pain.

Acute chest pain (ACP) is one of the most common symptoms in patients admitted to emergency departments (ED). It can be related to several life-threatening cardiovascular conditions such as acute coronary syndrome (ACS), aortic dissection, and pulmonary embolism. The optimal triage of patients with ACP is a clinical and healthcare necessity given the large number of patients daily admitted to ED with this symptom. The first contact with the patient in ED includes the clinical appraisal of the characteristics of ACP and coexisting symptoms, and the assessment of the patient's medical history. Risk scores may help stratify a patient's likelihood of having cardiac chest pain. The ECG examination allows the identification of patients with ST-segment elevation, depression, or T-wave changes, but may be normal in patients with non-ST-segment elevation ACS. Rapid protocols based on serial high-sensitivity cardiac troponin assays within one or two hours are recommended for identifying candidates for early discharge. Due to the bedside feasibility, non-invasiveness, and wide availability, transthoracic echocardiography represents the first-line imaging modality for evaluating patients with ACP. In selected cases, computed tomography angiography may also be performed. A practical approach to ACP in ED should improve patient outcomes and reduce healthcare system costs. This review aimed to provide an overview of the characteristics of patients with ACP of cardiac origin and to describe the state of the art about their management in the ED.

Risk-informed design: An unavailability allocation approach

Due to the technological complexity of nuclear power plants and the large number of components involved in their design, the objective space for balancing nuclear safety, availability performance, and cost in an optimization model becomes extremely vast. Consequently, this poses challenges in achieving operational optimization at the plant level, complicating the assurance of compliance with regulatory requirements. One solution is to partition the problem and analyze it at a system level to reduce the objective space. In this work, different options are analyzed to optimize cost and unavailability for systems that provide the means for meeting previously proposed unavailability targets. This ensures that licensing basis events (LBE) comply with risk targets and regulatory criteria. Additionally, the role of system unavailability due to maintenance activities and reliability issues related to unplanned component failures (e.g., random failures, common cause failures) in the optimization problem is analyzed. The various proposed optimization methods are implemented in the design of a pressurized water reactor (PWR) system. Evolutionary algorithms are used as optimization methods, with Genetic Algorithms for single-objective problems and Non-dominated Sorting Genetic Algorithm III (NSGA-III) for multi-objective problems. The main finding suggests that traditional models, which aim to minimize costs and unavailability, face challenges in adapting to the systems unavailability target. Therefore, optimization occurs by shifting the focus towards minimizing costs and distance to the unavailability target. This approach ensures that the results closely align with the unavailability target, thereby creating more efficiency in operating and maintenance costs while also ensuring acceptable design and operational regulatory compliance.