System Cost Research Articles

Retrofitting agricultural detention systems can economically enhance nitrogen treatment with payment for services approach

Despite their global presence and multiple services including water storage and treatment, limited field studies have focused on quantifying water quality effectiveness of agricultural stormwater detention areas (SDAs; farm ponds). Limited information that does exist stems from unverified modeling studies. Economics is another neglected aspect of SDAs. An SDA in the Everglades basin of Florida was monitored to quantify its baseline nitrogen (N) treatment. Design flaws of underutilized surface storage in variably ponded areas and short-circuiting were rectified by retrofitting the SDA through compartmentalization, increase in outflow structure control elevation, and installing channel plugs. Post-retrofit total N retentions doubled (7500 from 3700 kg), a similar trend was also observed in surface water volume indicating that water retention drives N retention. Targeting enhanced water retention through retrofits can achieve reductions in downstream nutrient flows. The cost of additional N treatment from retrofits was $12/kg, an order of magnitude less than the published costs for other detention systems including newly constructed wetlands ($180/kg) and tailwater recovery systems ($214/kg). A scale-up showed that retrofitting SDAs could reduce the N discharge to an estuary in the Everglades by more than 50 %. A win-win payoff to farmers for SDA retrofits through a payment for environmental services program can achieve nutrient treatment economically and facilitate large-scale adoption.

Size optimization of standalone wind-photovoltaics-diesel-battery systems by Harris hawks optimization (HHO): Case study of a wharf located in Bushehr, Iran

The global increase in energy demand has led to a growing focus on renewable energy sources as a potential solution. This study examines the annual total cost of optimized off-grid hybrid multi-resource systems, considering various configurations of Wind Turbines (WT), Photovoltaics (PV), Diesel Generators (DG), and Batteries (Bat). The research focuses on an oil dock in Bushehr, Iran, as a case study. The optimization process employs the Harris Hawk Optimization (HHO) algorithm – which is used for the first time for hybrid configuration and optimal sizing in this paper-, a nature-inspired, population-based optimization technique. This algorithm’s performance is compared to conventional optimization methods to assess its efficiency. The study’s methodology involves: (1) Explaining the economic relationships for each energy source, (2) Formulating a cost function, (3) Using the HHO algorithm to minimize the total cost of the renewable energy-based hybrid systems. The HHO algorithm is inspired by the hunting behavior of Harris hawks, specifically their “wonder attack” strategy. This novel approach to optimization aims to find the most cost-effective configuration of energy sources for the given scenario. Key findings of the study include the HHO algorithm demonstrated superior efficiency compared to Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO) across all configurations tested. The most cost-effective configuration was found to be a combination of photovoltaics, batteries, and diesel generators. This setup had the lowest total annual cost among all configurations examined. The optimal system consisted of 450 photovoltaic units, 9 battery units, and 2 diesel generator units, with a minimum annual cost of approximately $355,525. These results highlight the potential of the HHO algorithm in optimizing renewable energy systems and demonstrate the complex trade-offs between cost and environmental impact in hybrid energy configurations. The study contributes valuable insights to the field of renewable energy system design and optimization, particularly for off-grid applications in industrial settings.

A novel eight-switch nine-level modified ANPC inverter topology and its optimal modulation strategy

An eight-switch nine-level modified ANPC inverter (8S-9L-MANPC) is designed and simulated in this paper. With this topology, only one DC voltage source and eight active semiconductor switches are required, significantly reducing system size, weight, and cost. The structure consists of a five-level ANPC inverter cascaded with a two-level leg converter. This arrangement extends the 5L-ANPC structure to a 9L configuration without requiring the use of additional capacitors. To maintain the balancing of the flying capacitor (FC), a modulation strategy using a switching state redundancy-based control method is employed. Recent 9L inverters incorporating flying capacitors and based on a hybrid structure have been compared with the proposed structure. By conducting this comparison, we can highlight the potential improvements offered by the proposed structure over the recent 9L inverters in terms of reduced component count, simplified design, and cost savings. The proposed 8S-9L-MANPC inverter is tested under various operating situations in MATLAB/Simulink simulation.

The impact of extended decision times in planning and regulatory processes for energy infrastructure

Effective planning and regulatory processes ensure orderly energy systems. Often, decisions are not made within mandated timeframes and discrete approval processes are infrequent. A given delay may be compounded where multiple approval processes exist in sequence. These factors can negatively affect system costs and, ultimately, consumer welfare. We show that delays have meaningful impacts on electricity prices, system emissions, infrastructure investment and system operating costs. Our numerical simulation of data from Ireland shows that wholesale electricity prices are up to 10% higher and CO2 emissions up to 4% higher, attributable to delays associated with planning and regulatory approvals.

MANGOever: An optimization framework for the long-term planning and operations of integrated electric vehicle and building energy systems

The growing electrification of heating and mobility has increased the interdependence of these two sectors and introduced a new coupling with the electricity sector. However, existing studies on local energy planning often focus solely on solutions to meet buildings’ energy demands, neglecting or highly simplifying new mobility demands. Here, we address this gap by introducing MANGOever (Multi-stAge eNerGy Optimization for electric vehicles and energy retrofits), a comprehensive optimization framework for long-term co-planning of building energy systems and electric vehicle (EV) charging infrastructure. The framework optimizes multi-stage investments and operational strategies to minimize system costs and CO2 emissions over a multi-year horizon, considering the stochastic nature of EV charging based on observed driver habits and travel patterns. Applying the model to a case study of a multi-family home in Switzerland reveals significant synergies between EV charging and the management of solar photovoltaic generation. The results underscore the importance of considering habit-based EV charging behavior in the model and demonstrate how diverse EV plug-in behaviors can be leveraged to maximize the use of midday solar production and reduce emissions. These findings emphasize the need for integrated planning of these sectors to achieve a cost-effective, low-carbon energy transition.

Optimization and Uncertainty Analysis of Hybrid Energy Systems Using Monte Carlo Simulation Integrated with Genetic Algorithm

This study investigates the optimization of hybrid energy systems (HES) composed of wind turbines, battery banks, and diesel generators, focusing on addressing the challenges posed by wind speed uncertainty. This research contributes significantly to the field by developing a novel methodology that combines uncertainty analysis with hybrid optimization techniques to improve the reliability and cost-effectiveness of HES. The findings revealed that initial simulations without renewable energy sources result in high diesel consumption, with fuel usage reaching 534,810 liters per year and associated carbon emissions totaling 797,070 kg/year. Through optimization, an economically viable configuration is identified, consisting of 37 battery banks, two 250 kW wind turbines, and a 340-kW diesel generator, achieving an Annualized System Cost (ASC) of $166,500 and a Cost of Energy (COE) of $0.1480/kWh. The Monte Carlo simulations indicate a most probable COE of $0.1450/kWh for the wind turbine/battery/diesel system, occurring with an 8.3% probability, while approximately 90% of COE values fall below $0.1669/kWh. The average COE is $0.14834/kWh, with a minimum of $0.12163/kWh. The Renewable Energy Fraction (REF) spans from 28% to 97%, with an average of 64% and a standard deviation error of 9.6% at a 95% confidence level. The results underscore the potential implications for informing policymakers and industry leaders about the design and evaluation of HES under uncertain environmental conditions. By addressing the limitations of current approaches, this work contributes valuable insights into the economic, environmental, and social dimensions of hybrid renewable energy systems.

Laparotomy Versus Laparoscopy Cholecystectomy for Surgical Treatment in Patients with Gallstone Disease: A Comparative Cost-Effectiveness Analysis

Background: Due to the increasing costs of the healthcare system and limited financial resources, healthcare policymakers should adopt more cost-effective strategies. Objectives: This study aimed to compare the cost-effectiveness of laparotomy cholecystectomy with laparoscopy. Methods: This economic evaluation was conducted on patients with cholecystitis who were candidates for surgery in a private hospital in Ahvaz in 2021. Data collection tools consisted of four parts: (1) demographic information checklist; (2) clinical information checklist; (3) cost checklist; and (4) effectiveness assessment tool. SPSS22 and STATA14.2 were used for data analysis. One-way sensitivity analysis and Tornado diagrams were performed using Tree Age software. Results: The mean total effectiveness score in patients treated with laparoscopy was 83.44 (SD = 11.34), which was higher than those treated with laparotomy at 68.39 (SD = 13.61). This difference was statistically significant in all effectiveness criteria, except for postoperative infection rates and length of operation (P < 0.001). The mean cost for patients undergoing laparoscopy was significantly higher than for those undergoing laparotomy ($481.43 vs. $459.49). However, overall, laparoscopic treatment (5.77) was more cost-effective than laparotomy (6.71). The laparoscopic procedure was approximately $1.47 per effectiveness unit cheaper than laparotomy, according to the ICER. One-way sensitivity analysis showed that the laparoscopic method remained more cost-effective, even when adjusting for cost and effectiveness components. Conclusions: Although laparoscopic cholecystectomy was more expensive than laparotomy cholecystectomy, it was generally more cost- effective. The results of this research may assist Iran’s healthcare policymakers and managers in promoting laparoscopic cholecystectomy in hospitals.

Quantifying Uncertainty Costs in Renewable Energy Systems Considering Probability Function Behavior and CVaR at Low-Probability Generation Extremes using Deterministic Equations

The increase and integration of renewable energy sources in electrical power systems implies an increase in uncertainty variables, both in costs and production, of economic dispatch (ED) and currently have a significant influence on wholesale electricity markets (MEM). Uncertainty costs refer to the quantification of additional expenses or economic losses associated with the variability inherent in the generation of renewable energy, such as wind, solar, or hydroelectric. Therefore, this article presents deterministic equations related to cost overestimation and underestimation, as well as CVaR, to model and evaluate the stochasticity of risks associated with the integration of renewable sources, allowing system operators and planners to make informed decisions. To mitigate or use said risks in energy systems with high penetration of elements, mainly smart networks. In this study, a mathematical analysis is carried out using the histogram spectrum formed by the power generated by the probability density function (PDF) for solar generation, although it is possible to consider other types of functions to determine energy generation. The objective of the proposed model is to provide another tool to the system operator for energy management and planning, which relieves a little of the weight of the computational load and at the same time presents more precision in the results by being able to work with a database. Historical data if these values are available. Commonly, for this type of analysis, values are estimated using probabilistic calculations by density functions when integrating these functions, or in other recent cases by estimating them by analytical methods of the same functions. A validation of the model is presented by comparing the result with the Monte Carlo simulation, developing the total cost of uncertainty only from "low probability generation extremes". Furthermore, the results are presented through analytical uncertainty cost functions (AUCF). This analysis includes the calculation of uncertainty costs for low and high-probability energy generation, determined by the Conditional Value at Risk (CVaR), using deterministic equations.

The effectiveness and costs of intensive stroke rehabilitation and improvements in patient pathway in Finland: a retrospective benchmarking controlled trial.

To assess the effectiveness and costs of intensive stroke rehabilitation and improvements in patient pathway in the city of Lahti and in Päijät-Häme region compared with other parts in Finland. Retrospective benchmarking controlled trial. Three cohorts of Finnish community-dwelling patients (n = 94,749, n = 4,184, and n = 105,458) with ischaemic stroke between 2001 and 2019. This study is based on the PERFECT 2001-2019 database of ischaemic stroke patients. PERFECT indicators describe how the stroke patients recover. The difference-in-difference method was used in the main analysis. Improved stroke rehabilitation in Lahti increased the share of patients discharged home (p = 0.005) and decreased the length of first institutional episode (-4 days, p = 0.006), the share of patients institutionalized (-5.1%, p = 0.001), and the costs of first institutional episode (€-2,085, p < 0.001) compared with the rest of Finland. Discharges to home increased 6.6 percentage points (p = 0.021) in Lahti compared with rest of Päijät-Häme. After 2013, the costs of first institutional episode per patient in Päijät-Häme decreased significantly compared with the rest of Finland (p < 0.001). Investments in intensive stroke rehabilitation and patient pathway seem to provide both faster and better return to home for patients and reduced costs for the healthcare system.

The economics of translating a biosimilar from lab to market in India.

This study aims to establish a cost basis for biologics manufacturers and policymakers by quantifying the price and time required to bring a biosimilar from the lab to market. For efficient implementation of a cost-based policy, especially for life-saving medicines like biosimilars, it is imperative to establish a benchmark for the cost involved in biosimilar development. In this holistic and multiple-case study, stage-wise cost estimates of biosimilar development were obtained for microbial and mammalian systems. The investigation of six biopharmaceutical companies based in India concluded that biosimilar development through the microbial system costs ∼18 million USD and ∼21 million USD for the mammalian system. Additionally, 45-50 million USD is required as a one-time capital investment. Further, US/EU authorization can cost ∼25 million USD per product. Clinical studies are the most expensive and account for 60%-70% of total development cost. The presented information can serve as a basis for implementing cost-based pricing in countries like India and reimbursement policies for biosimilars under Medicare Part B in the United States.

Addressing Uncertainty in Renewable Energy Integration for Western Australia’s Mining Sector: A Robust Optimization Approach

The mining industry is a key contributor to Western Australia’s economy, with over 130 mining operations that produce critical minerals such as iron ore, gold, and lithium. Ensuring a reliable and continuous energy supply is vital for these operations. This paper addresses the challenges and opportunities of integrating renewable energy sources into isolated power systems, particularly under uncertainties associated with renewable energy generation and demand. A robust optimization approach is developed to model a multi-source hybrid energy system that considers risk-averse, risk-neutral, and risk-seeking strategies. These strategies address power demand and renewable energy supply uncertainties, ensuring system reliability under various risk scenarios. The optimization framework, formulated as a mixed integer linear programming problem and implemented in Python using the Gurobi Optimizer, integrates renewable energy sources such as wind turbines, photovoltaic arrays, and demand response programs alongside traditional diesel generators, boilers, combined heat and power units, and water desalination. The model ensures reliable access to electricity, heat, and water while minimizing operational costs and reducing reliance on fossil fuels. A comprehensive sensitivity analysis further examines the impact of uncertainty margins and the value of a lost load on the total system cost, providing insights into how different risk strategies affect system performance and cost-efficiency. The results are validated through three case studies demonstrating the effectiveness of the proposed approach in enhancing the resilience and sustainability of isolated power systems in the mining sector. Significant improvements in reliability, scalability, and economic performance are observed, with the sensitivity analysis highlighting the critical trade-offs between cost and reliability under varying uncertainty conditions.

A Single-Input Multi-Output Inverter with Voltage Boosting for Multi-Load Wireless Power Transfer Systems

Multi-load wireless power transfer systems generally require the configuration of multiple transmitting coils. Using traditional single-output inverters will increase the number of inverters, leading to increased system costs and complex structures. Therefore, this paper proposes a single-input multi-output inverter that can drive multiple transmitting coils simultaneously. Compared with traditional single-output inverters and existing multi-output inverters, the proposed inverter utilizes a topology improvement design and an efficient expansion method. By adding only one inductor, one capacitor, and a small number of power switches, it can generate multiple controllable and stable outputs while ensuring output gain, which is expected to simplify the system structure and improve system performance. This paper first introduces the topology evolution process and operating principle of the proposed inverter. Secondly, a mathematical model is established to analyze its operating characteristics, and its parameter design is carried out. Meanwhile, a comparison with existing multi-output inverters is conducted. Then, the resonant compensation networks are analyzed and selected to match the requirements of different loads. Finally, a simulation model of the proposed inverter is constructed, and an experimental setup is set up. The feasibility and superiority of the proposed inverter are verified through simulation and experiments.

Structural Modulation of Nanographenes Enabled by Defects, Size and Doping for Oxygen Reduction Reaction

Nanographenes are among the fastest‐growing materials used for the oxygen reduction reaction (ORR) thanks to their low cost, environmental friendliness, excellent electrical conductivity, and scalable synthesis. The perspective of replacing precious metal‐based electrocatalysts with functionalized graphene is highly desirable for reducing costs in energy conversion and storage systems. Generally, the enhanced ORR activity of the nanographenes is typically deemed to originate from the heteroatom doping effect, size effect, defects effect, and/or their synergistic effect. All these factors can efficiently modify the charge distribution on the sp2‐conjugated carbon framework, bringing about optimized intermediate adsorption and accelerated electron transfer steps during ORR. In this review, the fundamental chemical and physical properties of nanographenes are first discussed about ORR applications. Afterward, the role of doping, size, defects, and their combined influence in boosting nanographenes’ ORR performance is introduced. Finally, significant challenges and essential perspectives of nanographenes as advanced ORR electrocatalysts are highlighted.

Planning and Optimisation of Renewable Energy Systems for Decarbonising Operations of Oil Refineries

AbstractGiven the urgency to transition to low carbon future, oil refineries need to identify feasible strategies for decarbonisation. One way to address this is by integrating renewable energy systems. However, the high initial costs and intermittency appeared to be the key barriers for the adoption of renewable energy technologies. Hence, a multi-period optimisation model is developed via mixed integer linear programming in this work to determine the optimal renewable energy system in terms of cost and its optimal energy storage technology to enhance its flexibility for oil refinery operations. This model aims to minimise the costs of the renewable energy system while considering its ability to accommodate the varying energy demands across the time periods. An oil refinery case study is used to demonstrate the effectiveness of the developed model. The developed model is expected to propose an optimal renewable energy system that meets the energy demands and, at the same time, achieves the decarbonisation goal. Based on the results, the optimal renewable energy system comprises cost-effective technologies to generate various energy outputs including electricity, hydrogen, high-pressure and medium-pressure steam to meet energy demands. Additionally, the result of the case study shows that the integration of renewable energy systems achieves a reduction of 5,353 tonnes of carbon dioxide. Apart from that, the incorporation of energy-efficient energy storage results in a 10% reduction in the total cost of the optimal renewable energy system. Compressed hydrogen gas storage and battery were used to store excess hydrogen and electricity during periods with low demands and subsequently consumed during peak demand periods. This can, therefore, reduce the technological capacity required. With the aid of storage facilities, the flexibility of the renewable energy system is elevated in meeting varied demands, which otherwise would incur additional expenses.

Structural Modulation of Nanographenes Enabled by Defects, Size and Doping for Oxygen Reduction Reaction.

Nanographenes are among the fastest-growing materials used for the oxygen reduction reaction (ORR) thanks to their low cost, environmental friendliness, excellent electrical conductivity, and scalable synthesis. The perspective of replacing precious metal-based electrocatalysts with functionalized graphene is highly desirable for reducing costs in energy conversion and storage systems. Generally, the enhanced ORR activity of the nanographenes is typically deemed to originate from the heteroatom doping effect, size effect, defects effect, and/or their synergistic effect. All these factors can efficiently modify the charge distribution on the sp2-conjugated carbon framework, bringing about optimized intermediate adsorption and accelerated electron transfer steps during ORR. In this review, the fundamental chemical and physical properties of nanographenes are first discussed about ORR applications. Afterward, the role of doping, size, defects, and their combined influence in boosting nanographenes' ORR performance is introduced. Finally, significant challenges and essential perspectives of nanographenes as advanced ORR electrocatalysts are highlighted.

Women's Acceptability of and Satisfaction With Self-Monitoring Blood Pressure During Pregnancy.

The aim of this study was to evaluate the acceptability and satisfaction of pregnant women at risk of hypertensive disorders with self-monitoring their blood pressure. A prospective cross-sectional study. Women at risk of hypertensive disorders during pregnancy were recommended to self-monitor their blood pressure at the study site to reduce hospital visits. Women who purchased a subsidised blood pressure monitor through the hospital were invited to participate in a survey about acceptability of and satisfaction with self-monitoring their blood pressure between hospital visits. Data were analysed with descriptive statistics and content analysis. Thirty women completed the survey. Most women were highly satisfied with self-monitoring their blood pressure and the support provided from hospital staff for responding to high readings experienced at home. The reassurance of monitoring their health and the convenience of reduced hospital visits were reported benefits of self-monitoring blood pressure. Inaccuracy of readings, anxiety associated with high readings, and cost of purchasing the machine were described as concerns by a small number of women. The findings suggest that self-monitoring blood pressure may be a useful addition to antenatal care for women at risk of hypertensive disorders. Ensuring that antenatal care providers adequately test machines for accuracy and educate women about proper use is essential. Promoting equitable access to self-monitoring by providing free or very low-cost machines to women experiencing financial disadvantage is warranted. Self-monitoring blood pressure during pregnancy has the potential to improve the detection of hypertensive disorders and reduce hospital visits and associated healthcare system costs. The authors have nothing to report.

Evaluation of a complex intervention: the Latch On randomized controlled trial of multicomponent breastfeeding support for women with a raised body mass index.

Latch On's objective was to achieve improved breastfeeding rates in women with raised body mass indices using a multicomponent breastfeeding support intervention. A hybrid type 1 implementation-effectiveness trial with mixed-methods process and health economics analyses were conducted. Data collection included stakeholder questionnaires, interviews, focus groups, fidelity data, participant and health system costs. The intervention was delivered with fidelity but the high breastfeeding rates at 3months were not different between intervention and usual care. Participants receiving the minimum intervention dose were more likely to initiate breastfeeding (P=0.045) and be breastfeeding at hospital discharge (P=0.01) compared with participants below the threshold. Participant exit interview themes highlighted the importance of improving breastfeeding support to women, the effect of COVID-19 on the breastfeeding experience, and found that the intervention improved the experience of establishing breastfeeding. The intervention cost €157 per participant, with no other cost difference between groups. Process analysis found that follow-up breastfeeding services continued in half of sites after study completion. This low-cost intervention resulted in a more enjoyable breastfeeding experience for participants and changed practice in some study sites. The intervention dose received may impact effectiveness, but further research is needed to provide definitive evidence of clinical and cost effectiveness.

Abstract 4117927: Prevalence of Obstructive Sleep Apnea Among Consecutive Patients with Atrial Fibrillation Using WatchPAT Home Sleep Testing: A Prospective Cohort Study

Background: Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice and is associated with significant morbidity, mortality, and health system costs. Obstructive sleep apnea (OSA) is a highly prevalent condition affecting more than one third of the global adult population and, among patients with comorbid AF, contributes to poor AF related quality of life. However, the prevalence of undiagnosed OSA in the general AF population is unknown. Methods: In this pragmatic, phase IV prospective cohort study, we performed sleep testing on consecutively enrolled ambulatory AF patients for OSA using a cloud-based disposable home sleep test (the WatchPAT system). The primary outcome of our study was the prevalence of undiagnosed mild (5≤AHI&lt;15) or moderate/severe (AHI≥15) OSA in the general AF population. A prespecified power analysis supports 80% power to detect a &lt;5% difference in OSA prevalence compared to the general population (defined by AHI cut-offs of ≥5 or ≥15). Results: We performed sleep testing in 180 AF patients. Enrollment concluded early after a prespecified interim power analysis showed a larger than predicted difference of OSA prevalence between AF patients and the general population. Among the patients tested, 126 (70.4%) had paroxysmal AF, and 53 (29.6%) had persistent/long standing persistent AF. None had previous sleep testing. Our results show that the prevalence of OSA was 83.3% by AHI≥5 and 47.2% had moderate/severe OSA (AHI≥15). Patients with persistent or long standing persistent AF demonstrated even higher rates of OSA with 51 (96.3%) testing positive for OSA (AHI≥5) and 35 (64.2%) with moderate/severe OSA (AHI≥15). Conversely, standardized OSA screening instruments (Stop-BANG questionnaire or Epworth Sleepiness Scale) were poorly predictive of OSA in AF patients (AUC 0.66 and AUC 0.44, respectively). Conclusions: Undiagnosed OSA is highly prevalent in the general AF population. Our results demonstrate that OSA prevalence in AF patients exceeds that in the general population with a high degree of confidence. Widely used clinical prediction instruments cannot effectively be used to guide sleep testing decisions in AF patients. Our results suggest routine OSA testing should be performed in the comprehensive evaluation of all AF patients.

Integration of in-wheel motor sensorless systems and hierarchical direct yaw moment control for distributed drive electric vehicles

Ensuring robust and reliable control of distributed vehicles powered by in-wheel motor systems poses a significant challenge due to the harsh operating environments and high costs of such motor systems. Poor motor control, parameter variations, and sensor malfunction under these conditions can compromise the vehicle yaw stability. Integrating permanent magnet synchronous motor (PMSM) sensorless systems with vehicle yaw moment control offers a cost-effective solution for this issue without wheel angular speed sensors while enhancing yaw stability. In this paper, a composite nonlinear feedback sliding mode controller that can enhance the PMSM speed response is proposed. The proposed scheme exhibits a rotor speed overshoot and transient time of only 0.64% and 0.07s, respectively, which are smaller and shorter compared with other methods under motor parameter changes. Subsequently, the key states and tire-road friction coefficients required for vehicle control were estimated using sensorless rotor speeds and unscented Kalman filters, enabling the integration of the PMSM sensorless system with the vehicle yaw moment control. Additionally, a fuzzy adaptive hybrid sliding mode method is presented for yaw moment control enhancement. This method maintained the smallest sideslip angle root mean square error during double lane changes (0.4192 deg) compared with other methods. Analysis results show that different motor controllers and parameter changes significantly affect the vehicle dynamics performance. The proposed integrated scheme is feasible and effectively enhances the yaw moment control via high-performance sensorless PMSM systems.

Predicting the probability of failure in medicinesʼ public procurement

The quality and timeliness of medicinesʼ supply to the healthcare system through public procurement is an urgent task of public policy in all countries of the world, including Russia. Failure to close (failure of tenders, termination of already concluded contracts) procurement procedures in such a socially significant area carries risks for the population, provokes the emergence of hidden transaction costs for the budget system to eliminate the consequences of procurement failures. In their previous works, the authors identified the factors that lead to the failure to close tenders for the purchase of remedies, and initially assessed the consequences of their influence on procurement procedures. The purpose of this article is to present a mathematical model of the probability of non-closure tenders based on the obtained results. To achieve this goal, through processing more than 1 million notifications of public procurement of remedies for 2022–2023, collected from the open sources, the tasks with no methodological solutions were completed. Thus, a set of features accompanying the failure to close procedures for the purchase of remedies was compiled. The composition of identified features was analyzed; their influence on non-closure of the procedure was assessed. A model of the probability of non-closure of the procedure was constructed, and the results were interpreted. Unlike previously published studies, the forecast model was implemented on the ensembles of decision trees using gradient boosting. This made possible to significantly improve the quality of the forecast for each factor affecting the probability of non-closure of the bidding. The results obtained in the article are not only scientifically novel, but can also be used by regulatory and control bodies in public procurement to develop methodological recommendations for customers on establishing optimal conditions for concluding and fulfilling contracts, which will reduce procurement risks and damage to the state budget.