Optimal Size Research Articles

Wehnelt photoemission in an ultrafast electron microscope: Stability and usability

We tested and compared the stability and usability of three different cathode materials and configurations in a thermionic-based ultrafast electron microscope: (1) on-axis thermionic and photoemission from a custom 100 μm diameter LaB6 source with a graphite guard ring, (2) off-axis photoemission from the Ni aperture surface of the Wehnelt electrode, and (3) on-axis thermionic and photoemission from a custom 200 μm diameter polycrystalline Ta source. For each cathode type and configuration, including the Ni Wehnelt aperture, we illustrate how the photoelectron beam-current stability is deleteriously impacted by simultaneous cooling of the source following thermionic heating. Furthermore, we demonstrate usability via collection of parallel- and convergent-beam electron diffraction patterns and by formation of the optimum probe size. We find that usability of the off-axis Ni Wehnelt-aperture photoemission is at least comparable to on-axis LaB6 thermionic emission, as well as to on-axis photoemission [the heretofore conventional approach to ultrafast electron microscopy (UEM) in thermionic-based instruments]. However, the stability and achievable beam currents for off-axis photoemission from the Wehnelt aperture were superior to that of the other cathode types and configurations, regardless of the electron-emission mechanism. Beam-current stability for this configuration was found to be ±1% (one standard deviation from the mean) for 70 min (longest duration tested), and steady-state beam current was reached within the sampling-time resolution used here (∼1 s) for 15 pA beam currents (i.e., 460 electrons per packet for a 200 kHz repetition rate). Repeatability and robustness of the steady-state condition were also found to be within ±1% of the mean. We discuss the implications of these findings for UEM imaging and diffraction experiments, for pulsed-beam damage measurements, and for practical switching between optimum conventional TEM and UEM operation within the same instrument.

Analysis of size effect and its influencing factors of brittle red sandstone with different heights

We carried out uniaxial compression tests on brittle red sandstone with different heights. The test results show that the uniaxial compressive strength of rock sample increases first and then tends to be stable with the increase of the size, which is approximately stable between 75 and 81 MPa. Both elastic energy and dissipated energy increase with the increase of rock sample size. In order to further analyze the mechanism behind these phenomena, we combined advanced numerical simulation and theoretical analysis to explain these phenomena, and systematically analyzed the end face effect as one of the key factors affecting the uniaxial compression characteristics of brittle red sandstone for the first time. Small sized rock samples are very sensitive to end effect. The middle of the large sized rock samples is in a uniform compression state, and the effect of end effect is weakend. When there are rigid pads at both ends of the rock sample, there is an obvious elastic vertebral body during the loading process of the rock sample. The bearing capacity of rock samples with rigid pads is greater than that of rock samples without rigid pads, and the energy released during instantaneous failure of rock samples without rigid pads is greater than that of rock samples with rigid pads. The findings of this paper make a valuable contribution to establishing optimal study sample sizes and advancing the utilization of laboratory test mechanics parameters in engineering applications.

The scale effects of agricultural credits, institutional governance and microfinance sustainability in Sub-Saharan African countries

PurposeThe study examines the synthesized influences of institutional governance and the scale effects of agricultural credits on the sustainability of microfinance institutions (MFIs) in Sub-Saharan Africa.Design/methodology/approachUsing a sample of 840 MFIs (300 independent and 540 networked), the study applied Generalized Method of Moments (GMM) and Lewbel’s estimator.FindingsResults indicate positive effects of financial structure, efficiency and agricultural credit scale on sustainability, with a non-linear U-shaped relationship between agricultural credit size and microfinance sustainability. Depending on institutional governance quality, a threshold is identified where agricultural credit scale significantly enhances the quality of Portfolio at Risk (lnPAR) in independent MFIs and Returns on Assets (lnROA) in networked MFIs.Research limitations/implicationsStudy suggests strengthening governance for transparency and operating within optimal size for enduring sustainable performance. While focused on Sub-Saharan Africa, future research could expand to various economies or introduce additional variables for a comprehensive analysis.Practical implicationsMFIs can achieve sustainability by implementing management guided by better institutional norms, innovative financial transformations better suited to financing agricultural activities and techniques and an organizational structure more aligned with their performance targets.Social implicationsBroader and more reliable access to financial services, particularly in the agricultural sector, can stimulate production and alleviate poverty.Originality/valueThe study’s originality lies in its contribution to the literature by examining the role of institutional governance in microfinance institution performance and evaluating microfinance in a broader Sub-Saharan African context, proposing threshold limits where agricultural microcredit compromises performance.

Enhancing the Performance of LiFePO4 Cathodes in Li-ion Batteries: Role of Surface Coating Based on ZIF-8 Particle Size Optimization

Enhancing the Performance of LiFePO4 Cathodes in Li-ion Batteries: Role of Surface Coating Based on ZIF-8 Particle Size Optimization

Enhancing the Performance of LiFePO4 Cathodes in Li-ion Batteries: Role of Surface Coating Based on ZIF-8 Particle Size Optimization

Enhancing the Performance of LiFePO4 Cathodes in Li-ion Batteries: Role of Surface Coating Based on ZIF-8 Particle Size Optimization

A novel optimization model for tackling capacity challenges in natural gas gathering systems

In the layout optimization of natural gas gathering pipeline network, a lot of work has been done, but few scholars focus on the capacity optimization of gas gathering station (short for station). The structural relationship parameters of dendritic pipeline network and the flow parameters of pipeline network are complicated, which makes it difficult to establish the model, and at the same time, the existence of nonlinear term causes the difficulty of solving the problem. In the current two stage star-dendritic natural gas gathering pipeline network, pipeline sizes and station capacity are not well taken into account for the optimization of pipeline network layout at the same time. In this paper, a consideration station capacity and pipeline sizes: Two-stage star-dendritic network layout optimization model (SCPSTSDLO-Model) is constructed by integrating the factors of site discrete characteristics, station capacity and pipeline sizes, and considering the hydraulic pressure drop conditions of the network, and a solution framework based on the nonlinear processing is proposed, which can obtain the optimization results of optimal affiliation, station type, station site and pipeline sizes at the same time. Five cases are used to verify the validity and accuracy of the model and algorithm, compare the sensitivity analysis and solution algorithm, compare the hydraulic pressure drop conditions, and analyze the application in different forms of pipeline networks. The results show that the SCPSTSDLO-Model and the solution method established in this paper can obtain the optimal solutions under different topologies according to the application requirements, taking into account the capacity of stations and pipeline sizes.

AN EFFICIENT STUDY OF PV/WIND/BATTERY/ELECTROLYZER/H2-TANK/FC FOR A REMOTE AREA ELECTRIFICATION

With the rapid development of the use of renewable energy systems, it is becoming more and more important to combine different sources into hybrid renewable energy systems. Many parameters in hybrid renewable energy systems must be optimized to effectively size hybrid system components to achieve economic, technical, and design goals practically. This paper focuses on the optimal configuration of off-grid hybrid renewable energy systems (HRES). The system consists of a photovoltaic (PV), wind turbine (WT) and battery bank (BB), fuel cell (FC) with hydrogen tank (H2-tank), and electrolyzer (Elect). The optimal size of the proposed HRES component is achieved using a novel meta-heuristic technique called the whale optimization algorithm (WOA). WOA improves the optimal configuration of HRES to produce the best minimum value of the fitness function, which converges to the global optimal solution after several iterations. The proposed WOA is used to solve the multi-objective optimization problem of the cost of energy (COE) in $/kWh and the total net present cost (TNPC) in $. Two recent algorithms, particle swarm optimization (PSO) and gray wolf optimizer (GWO), have also been implemented in this work to demonstrate the effectiveness of the proposed algorithm.

Enhancement of CO2/N2 separation in MMMs by the regulation of nanofiller microenvironment

Mixed-matrix membranes (MMMs) incorporating Metal-Organic Frameworks (MOFs) represent a promising approach for gas separation, which can combine the advantages of selective adsorbents and processable polymers, and display great potential in the gas separation process. However, the further application of MMMs is severely impeded owing to the non-ideal interface between fillers and polymer matrix. In this work, a series of MIL-101(Cr) nanofillers, equipped with different kinds of counter anions were introduced to regulate the chemical microenvironment of the MMMs and improve the interfacial relationship between inorganic filler and polymer. The filler of MIL-101-HNO3 exhibits improved dispersibility in solvents and heightened interface relationship with the polymer matrix (PIM-1) due to its optimal particle size and distinctive exposure of crystallographic planes. Moreover, gas sorption isotherms reveal that the MIL-101-HNO3 MOF show reduced N2 adsorption capacity. Therefore, the MOF filler of MMMs were optimized rationally by microenvironment regulate strategy, leading to the MIL-101-HNO3/PIM-1 MMMs achieve high CO2 permeability and exceptional CO2/N2 ideal selectivity (CO2 = 14879 barrer, CO2/N2 = 24.3), surpassing the latest 2019 upper bound. That makes MIL-101-HNO3/PIM-1 a potential candidate for the selective removal of CO2 from stack gas and provides new insights into the regulation of the MMMs nanofiller microenvironment.

Optimizing capacitor size and placement in radial distribution networks for maximum efficiency

As distribution systems continue to expand, they face challenges such as increased system losses and inadequate voltage regulation. To address these issues, shunt capacitors are being deployed in distribution networks. These capacitors offer reactive power compensation, enhance power factor, improve voltage profiles, promote system stability, and significantly reduce losses. However, determining the appropriate capacitor sizes and their optimal placements requires careful consideration of both technical and economic factors. The nonlinear nature of optimal capacitor placement and sizing, leveraging optimization techniques becomes crucial in identifying the best locations and values for capacitors. This paper demonstrates the effective utilization of Particle Swarm Optimization (PSO) and Real Coded Genetic Algorithm (RCGA) optimization techniques for capacitor placement and selection. The optimization techniques are applied to a 33-bus IEEE standard radial distribution system, to reduce the real power loss and to improve the voltage profile considering both constant and variable loads. Both PSO and RCGA algorithms identify suitable locations for the placement of capacitors for reactive power compensation within the distribution system. By optimizing the objective function associated with capacitor placement costs and maximizing annual cost savings, the PSO and RCGA techniques yield promising results. After implementing the optimal capacitor placements at the identified candidate nodes, a significant reduction in losses within the radial distribution system is observed. Moreover, the cost savings achieved through optimal placement and sizing are substantial.

Multi-objective sizing and dispatch for building thermal and battery storage towards economic and environmental synergy

The role of building thermal and battery storage is pivotal in advancing smart cities and achieving sustainability goals through effective energy management. Despite their significance, there are several limitations in the sizing approach and value stream analysis with various objectives for their widespread adoption in buildings. This work proposes a flexible and scalable multi-objective optimization framework for optimal sizing and dispatch of building thermal and battery storage, addressing multiple objectives simultaneously using mixed-integer linear programming. The weighted-sum method is adapted, combining multiple objectives into a single function. The two-stage procedure iterates over different weights, generates optimal solutions, and forms the Pareto front. Case studies are performed to assess the energy, economic, and environmental benefits of building energy storage systems for a large office building in three climate locations. The results demonstrate that the proposed framework efficiently determines optimal sizing and dispatch strategies, addressing the balance between economic viability and emission reduction. The dynamic relationship between time-of-use energy charges and emission factors leads to diverse strategies based on whether economic or environmental concerns are prioritized. This research enhances our understanding of the benefits of thermal and battery storage systems in buildings, providing valuable guidance to stakeholders.

Independent fitness consequences of group size variation in Verreaux’s sifakas

The costs and benefits of group living are also reflected in intraspecific variation in group size. Yet, little is known about general patterns of fitness consequences of this variation. We use demographic records collected over 25 years to determine how survival and reproductive success vary with group size in a Malagasy primate. We show that female reproductive rates of Verreaux’s sifakas (Propithecus verreauxi) are not affected by total group size, but that they are supressed by the number of co-resident females, whereas mortality rates are significantly higher in larger groups. Neither annual rainfall nor the adult sex ratio have significant effects on birth and death rates. Hence, these sifakas enjoy the greatest net fitness benefits at small, and not the predicted intermediate group sizes. Thus, independent fitness proxies can vary independently as a function of group size as well as other factors, leading to deviations from optimal intermediate group sizes.

Advancing sustainability: An integrated decision support framework for fleet selection in open pit mining construction

The process of selecting and configuring equipment fleets for open-pit mining is complex, requiring consideration of both quantitative and qualitative criteria. This paper presents a novel comprehensive framework for selecting loading/hauling fleets in open-pit mining projects. Through an extensive review of the literature, a total of 21 selection criteria are initially identified. With guidance from local experts, these criteria are shortlisted into five primary groups: site conditions, managerial, social, and environmental criteria. The ranking of the identified selection criteria is carried out by employing the analytical hierarchy process (AHP) and multi-attribute utility theory (MAUT) methods, utilizing a questionnaire survey. The most crucial criteria in selecting loading/hauling equipment are identified as the equipment's operating cost, productivity, repair and maintenance costs, and soil condition. An optimization module is developed to optimize the selected fleet, while adhering to sustainability principles and project constraints. The optimization module considers social, economic, and environmental sustainability variables to determine the optimal number, size, type, and capacity of fleets needed to complete the work within the allotted time. As a case study, the proposed framework is validated by experts in these types of projects. The framework's ability to effectively select fleet units for sustainable loading/hauling fleet is confirmed through this validation method.

Hydrogen storage integrated in off-grid power systems: a case study

This paper investigates the feasibility and benefits of integrating hydrogen storage systems into off-grid power systems. As a case study, a stand-alone microgrid located on a small island in southeastern Sardinia (Italy) and already equipped with a photovoltaic (PV) system coupled with batteries is chosen.To evaluate the integration benefits of the two storage systems (hydrogen and batteries) and the optimal sizing of the hydrogen storage section, a parametric analysis with a simulation model implemented in the MATLAB environment has been carried out. Results show that the optimal integration between the two storage systems is found by imposing a share of the batteries (18 kWh, 50% of the overall battery capacity) to exclusively supply the load demand (called battery energy buffer). In these conditions, an almost 100% self-sufficiency of the microgrid can be achieved by a hydrogen generator with the lowest size considered (2.4 kW), a hydrogen storage volume of 10 m3 and a fuel cell, mainly able to completely cover the night loads, of 1.5 kW. This sizing leads to a Levelized Cost of Electricity (LCOE) for the hydrogen section of about 10.5 €/kWh.

Enhancing residential energy access with optimized stand-alone hybrid solar-diesel-battery systems in Buea, Cameroon

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system’s dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.

Improvement of sand-washing performance and internal flow field analysis of a novel downhole sand removal device

With the progression of many shale gas wells in the Sichuan-Chongqing region of China into the middle and late stages of exploitation, the problem of sand production in these wells is a primary factor influencing production. Failure to implement measures to remove sand from the gas wells will lead to a sharp decline in production after a certain period of exploitation. Moreover, As the amount of sand produced in the well increases, the production layer will be potentially buried by sand. To boost the production of shale gas wells in the Sichuan-Chongqing region and improve production efficiency, a novel downhole jet sand-washing device has been developed. Upon analyzing the device's overall structure, it is revealed that the device adopts a structural design integrating a jet pump with an efficient sand- washing nozzle, providing dual capabilities for jet sand- washing and sand conveying via negative pressure. To enhance the sand- washing and unblocking performance of the device, various sand- washing fluids and the structures of different sand- washing nozzles are compared for selection, aiming to elevate the device's sand- washing and unblocking performance from a macro perspective. Subsequently, drawing on simulation and internal flow field analysis of the device's sand- washing and unblocking process through CFD and the control variable method, it is ultimately found that the length diameter ratio of the cylindrical segment of the nozzle outlet, the outlet diameter, and the contraction angle of the nozzle greatly influence the device's sand- washing and unblocking performance. And the optimum ranges for the length-diameter ratio of the cylindrical segment of the nozzle outlet, the outlet diameter, the contraction angle of the nozzle, and the inlet diameter are 2 to 4, 6 mm to 10 mm, 12° to 16°, and 18 mm and 22 mm, respectively. The findings of the research not only provide new insights into existing sand removal processes but also offer a novel structure for current downhole sand removal devices and a specific range for the optimal size of the nozzle.

Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.

To circumvent regulatory barriers that limit medical data exchange due to personal information security concerns, we use homomorphic encryption (HE) technology, enabling computation on encrypted data and enhancing privacy. This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research, focusing on the integration feasibility across institutions and determining the optimal size of hospital data sets for improved prediction models. We used data from 341,007 individuals aged 18 years and older who underwent noncardiac surgeries across 3 medical institutions. The study focused on predicting in-hospital mortality within 30 days postoperatively, using secure logistic regression based on HE as the prediction model. We compared the predictive performance of this model using plaintext data from a single institution against a model using encrypted data from multiple institutions. The predictive model using encrypted data from all 3 institutions exhibited the best performance based on area under the receiver operating characteristic curve (0.941); the model combining Asan Medical Center (AMC) and Seoul National University Hospital (SNUH) data exhibited the best predictive performance based on area under the precision-recall curve (0.132). Both Ewha Womans University Medical Center and SNUH demonstrated improvement in predictive power for their own institutions upon their respective data's addition to the AMC data. Prediction models using multi-institutional data sets processed with HE outperformed those using single-institution data sets, especially when our model adaptation approach was applied, which was further validated on a smaller host hospital with a limited data set.

Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.

To circumvent regulatory barriers that limit medical data exchange due to personal information security concerns, we use homomorphic encryption (HE) technology, enabling computation on encrypted data and enhancing privacy. This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research, focusing on the integration feasibility across institutions and determining the optimal size of hospital data sets for improved prediction models. We used data from 341,007 individuals aged 18 years and older who underwent noncardiac surgeries across 3 medical institutions. The study focused on predicting in-hospital mortality within 30 days postoperatively, using secure logistic regression based on HE as the prediction model. We compared the predictive performance of this model using plaintext data from a single institution against a model using encrypted data from multiple institutions. The predictive model using encrypted data from all 3 institutions exhibited the best performance based on area under the receiver operating characteristic curve (0.941); the model combining Asan Medical Center (AMC) and Seoul National University Hospital (SNUH) data exhibited the best predictive performance based on area under the precision-recall curve (0.132). Both Ewha Womans University Medical Center and SNUH demonstrated improvement in predictive power for their own institutions upon their respective data's addition to the AMC data. Prediction models using multi-institutional data sets processed with HE outperformed those using single-institution data sets, especially when our model adaptation approach was applied, which was further validated on a smaller host hospital with a limited data set.

Size and topology optimization of truss-like structures under seismic excitations incorporating probabilistic aspects

Size and topology optimization of truss-like structures under seismic excitations incorporating probabilistic aspects

O-219 Explainable artificial intelligence (XAI) to determine the follicle sizes on trigger day that maximize mature oocyte yield

Abstract Study question Which follicle sizes on trigger day (TD) are key to obtaining mature oocytes, embryos, and blastocysts, thereby enhancing live birth rates? Summary answer Follicles sized 13-18mm are most likely to yield mature oocytes. Live birth rate (LBR) was increased with a greater proportion of follicles in this range. What is known already At the time of oocyte retrieval, follicles that are either too small or too large are less likely to yield mature oocytes. However, there is only limited data examining which follicle sizes on TD administration are optimally sized to yield mature oocytes, nor any data examining whether live birth rates are impacted. Explainable artificial intelligence (XAI) offers a valuable opportunity to leverage clinical data to determine the range of follicle sizes that are most likely to yield mature oocytes and lead to improved downstream outcomes. Study design, size, duration A retrospective study incorporating 19,082 patients undergoing their first IVF/ICSI cycle (2005-2023) across 11 European clinics. Follicle sizes on TD were related to oocyte maturity (14,140 patients), fertilization (17,822 patients), high-quality blastocyst development (17,488 patients), and pregnancy outcomes (12,672 patients). Secondary analyses included stratification by age (>35 years: 4,717 patients; ≤35 years: 5,707), and suppressant protocol (GnRH agonist: n = 5,420 patients; GnRH antagonist: n = 3,982). Participants/materials, setting, methods An ensemble-based XAI model, developed through ‘internal-external validation’, was trained, Bayesian optimized, and independently iteratively tested on hold-out clinic datasets. The model determined follicle sizes contributing most to successful laboratory outcomes by harnessing permutation importance and ‘SHAP’ values. Model performance was evaluated and optimized for mean absolute error (MAE). The top 50% contiguous cohort of influential follicle sizes on TD were identified, and further validated through analysis of follicle sizes on preceding days. Main results and the role of chance XAI analysis revealed that follicles sized 13-18mm on TD were most influential in yielding mature oocytes, especially those sized 15-18mm (MAE: 3.60 ±0.35). Predictive follicle sizes from scans 1-2 days preceding TD administration were consistent with expected mean follicle growth rates. For high-quality blastocysts, follicles sized 14-20mm were contributory, narrowing to 15-18mm in the subset of patients where oocytes had been confirmed to be mature. In hCG-triggered cycles, follicles sized 12-19mm were most likely to yield mature oocytes in GnRH antagonist cycles, whereas follicles sized 14-20mm were most contributory in GnRH agonist cycles. In patients ≤35 years, follicles 13-18mm were most contributory, whereas a wider range of follicles (11-20mm) were contributory in patients >35 years. In line with current practice, having three lead follicles ≥17mm on TD was associated with a median improvement in mature oocyte yield of 10% versus those that did not (p < 0.0001). By comparison, maximizing the proportion of follicles on TD within the optimal size range could better improve mature oocyte yield, for example, by 42% when at least 70% of follicles were sized 15-18mm (p < 0.0001). Likewise, LBR improved as the proportion of follicles sized 13-18mm was increased (<10%: 23.3%; 10-20%: 25.7%; 20-30%: 28.8%; 30-40%: 31.6%). Limitations, reasons for caution These data could help guide when to administer the trigger to optimize clinical outcomes, however prospective assessment of different TD strategies based on the proportion of follicles within the optimal follicle size ranges identified in comparison to current practice based on lead follicle size is required prior to clinical adoption. Wider implications of the findings These findings provide a data-driven approach to personalizing ovarian stimulation in IVF. The findings generated by applying an XAI model could assist clinicians in optimizing TD timing, potentially enhancing clinical outcomes by moving beyond the conventional focus on lead follicle size. Trial registration number not appicable

P-686 Optimal follicle size for triggering modified natural frozen embryo transfers

Abstract Study question What is the optimal follicle size threshold for performing trigger in modified natural cycle frozen embryo transfers (mNC-FET) cycles Summary answer The optimal follicle size threshold for performing trigger is 16 or 17, depending on the clinic’s preference to risk too early or too late triggering What is known already Recent studies reported higher fetal and maternal obstetrical complication rates are observed in artificial cycles compared to natural cycle frozen embryo transfers (NC-FET), making the natural cycle (NC) the preferred method. The modified natural cycle (mNC) provides a partial solution to overcome the disadvantages of the NC, mostly its repeated visits and inability to conveniently schedule the transfer. In a recently published large RCT, pregnancy rates between NC and mNC were similar, but many patients allocated to the mNC group had an LH surge prior to the hCG injection. Study design, size, duration A retrospective study consisting of 5,862 NC-FET was performed between 2018 and 2023 in a large tertiary hospital. Participants/materials, setting, methods Statistical analysis was conducted to determine the optimal follicle size threshold for triggering, for each threshold between 12-20, under the following assumptions: Clinic is closed on Sundays, ultrasound is initially performed on day 8 of the cycle and every 2 days afterwards, trigger is performed if the follicle size is at or above the threshold, unless clinic is closed 2 days after the current test day in which case no trigger was given. Main results and the role of chance The analysis measured 2 main outcomes for each trigger size threshold: The probability of triggering too early, defined as shifting the ovulation by 3 or more days. The probability of spontaneous LH surge to occur before triggering, possibly leading to transfer cancelation due to clinic closure on the designated transfer day. The traditional follicle size-based algorithm performed as follows: [Follicle size threshold]: ] Early trigger probability], [Closed day transfer probability] 12: 71.9%, 0.1% 13: 61.7%, 0.1% 14: 49.0%, 0.2% 15: 33.9%, 0.6% 16: 21.3%, 1.3% 17: 12.9%, 2.8% 18: 7.3%, 4.5% 19: 3.4%, 6.7% 20: 2.2%, 8.6% As can be seen from the results the optimal trigger threshold is either 16 or 17, depending on whether reducing transfers on days the clinic is closed has a higher importance than preventing a very early trigger day. Limitations, reasons for caution This was a retrospective study that performed statistical analysis to determine the probability of achieving each result based on the described logic. A prospective randomized clinical trial is warranted to validate the algorithm’s performance and consider variables that could not be accounted for, such as the patient’s endometrial width. Wider implications of the findings NC-FET protocols require the ability to adapt the transfer to clinics’ requirements. Given a traditional threshold-based methodology for triggering, a threshold of 16-17 is optimal depending on the risk preference of a clinic. Utilizing more advanced artificial intelligence algorithms based on ovulation prediction can significantly reduce both risk types. Trial registration number Not applicable