Transfer Probability Research Articles

Relational Integration Training Modulated the Frontoparietal Network for Fluid Intelligence: An EEG Microstates Study.

Relational integration is a key subcomponent of working memory and a strong predictor of fluid intelligence. Both relational integration and fluid intelligence share a common neural foundation, particularly involving the frontoparietal network. This study utilized a randomized controlled experiment to examine the effect of relational integration training on brain networks using electroencephalogram (EEG) and microstate analysis. Participants were randomly assigned to either a relational integration training group (n = 29) or an active control group (n = 28) for one month. The Sandia matrices task assessed fluid intelligence, while rest-EEG was recorded during pre- and post-tests. Microstate analysis revealed that, for microstate D, the training group demonstrated a significant increase in occurrence and contribution following the intervention compared to the control group. Additionally, microstate D occurrence was negatively correlated with reaction times (RTs). Post-training, the training group showed a lower occurrence and contribution of microstate C compared to the control group. Regarding transfer probability, the training group exhibited a decrease between microstates A and B, and an increase between microstates C and D. In contrast, the control group showed increased transfer probability between microstates A, B, and C, and a decrease between microstate D and other microstates (B and A). These findings indicate that relational integration training influences frontoparietal networks associated with fluid intelligence. The current study suggests that relational integration training is an effective intervention for enhancing fluid intelligence.

Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction.

Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding infants. This study introduces an explainable machine learning model to predict the risk of chemical transfer through human milk. Our novel framework integrates ensemble resampling methods with advanced feature selection techniques, addressing data imbalance and enhancing predictive accuracy. The balanced random forest classifier, optimized using the genetic algorithm for feature selection, achieved an area under the receiver operating characteristic curve (AUC) of 0.8708 and an accuracy of 82.67 % on the internal test set, with an accuracy of 86.36 % on the external validation set. The integration of the SHapley Additive exPlanations approach provided deeper insights by revealing how specific chemical properties influence the transfer of high-risk compounds into breast milk. This enhanced interpretability offers a clearer understanding of the associated risks and informs strategies for their mitigation. Structural alert analysis further identified molecular fragments linked to high-risk chemicals, enabling targeted risk assessments. Additionally, the model was applied to evaluate the transfer risks of FDA-approved drugs from 2019 to 2024, identifying several with high transfer probabilities. To broaden its application, we developed an online prediction tool that offers real-time risk assessments, providing an accessible resource for healthcare professionals and researchers. These contributions present a robust, ethically sound tool for assessing chemical exposure risks in breastfeeding infants, supporting informed decisions on drug use and environmental contaminant exposure.

Economic Evaluation of Crop Transition Patterns in Ananthapur District, Andhra Pradesh, India

The study aims to examine the crop shifts in Anantapur district of Andhra Pradesh. Anantapur is the southern-most district of the Rayalseema region of Andhra Pradesh. While agriculture remains the most important economic activity of the district, it is characterized by high levels of instability and uncertainty. Being in the rain-shadow region of Andhra Pradesh, the district is drought-prone. The results revealed that the groundnut which is an important crop in the district recorded a negative and non-significant growth rate of 0.69 per cent in area, but the productivity and production were significant. The result of Markov chain analysis shown that groundnut was the most stable crop with high retention probability compared with other crops in the district. The crops from which groundnut gained were bengalgram, redgram, jowar, chillies and sunflower but with varying transfer probabilities. The area under paddy was significantly influenced by rainfall. The area of ragi, cotton and bengalgram were influenced by their own lagged prices.

Flexible Local Differential Privacy Mechanism for Collecting Location Data

To address issues in existing location data collection methods, such as poor data utility and large deviations between perturbed and true locations, this paper proposes a personalized local differential privacy (LDP) mechanism for location data collection. Users can select their privacy protection range based on their needs, limiting the perturbed output to this range, thereby improving data utility. To address the issue of large deviations between perturbed and true locations, we introduce a new strategy where the location is perturbed with a probability that is higher the closer it is to the true location. By analyzing the mutual information upper bound of the true and estimated location distributions, the optimal perturbation probability range is determined. Finally, a probability transition matrix is generated from the location transfer probabilities, and the true location distribution is estimated from the perturbed location distribution.

MMG4: Recognition of G4-Forming Sequences Based on Markov Model.

G-quadruplexes (G4s) are special nucleic acid structures with various important biological functions. Existing tools and technologies for G4-forming sequences recognition are limited to time-consuming and costly methods such as circular dichroism and nuclear magnetic resonance. Developing a fast and accurate model for G4-forming sequences recognition has far-reaching significance. In this study, MMG4, a novel model to recognize G4-forming sequences based on Markov model (MM), was developed and the phenomenon of high recognition accuracy in the central region of the sequence and low accuracy in the two end regions was discovered. It was further found that the differences in base transfer probabilities, ratio distribution, and G4-motif structural content in different regions may be the causes of this phenomenon. The study also explored the impact of sequence length on recognition accuracy and found the optimal recognition interval to be [910-1049], with the highest recognition accuracy reaching 85.95%. By extracting sequence features, the study constructed three types of machine learning models: random forest (RF), support vector machine, and back-propagation neural network. It was found that recognition performance of MM was significantly better than that of the other three machine learning models, proving that the recognition method based on MM can effectively capture the correlation information between adjacent nucleotides of G4. By combining MM with the three machine learning models, the predictive performance of MMG4 improved. Among them, the RF model combined with MM has the best performance, achieving an area under the receiver operating characteristic curve value of 0.93 and an area under the precision-recall curve value of 0.9. Finally, the study validated the model robustness and generalization ability through independent testing dataset.

Research on Enterprise Emission Risk Assessment Model Based on XGBoost-AHP

This study aims to address the immature development of environmental pollution-related insurance in China by introducing machine learning technology to improve the traditional risk assessment model in order to scientifically and accurately quantify the pollution caused by enterprises in the production process. In this paper, public data such as annual reports and social responsibility reports of listed companies are collected, and the data on corporate emissions are identified by KMeans and GMM clustering, and then the probability of corporate illegal emissions behavior is predicted by using XGBoost classifier. At the same time, considering the environmental pollution risk of the location of the enterprise, Random Forest was used to predict the comprehensive risk and transfer probability. Finally, the enterprise discharge risk is evaluated by hierarchical analysis method. This study not only helps the enterprise's own risk management, but also provides a pricing basis for the insurance company and promotes the development of environmental pollution-related insurance in China, which has important theoretical and practical significance.

Large data density peak clustering based on sparse auto-encoder and data space meshing via evidence probability distribution

The development of big data analysis technology has brought new development opportunities to the production and management of various industries. Through the mining and analysis of various data in the operation process of enterprises by big data technology, the internal associated data of the enterprises and even the entire industry can be obtained. As a common method for large-scale data statistical analysis, clustering technology can effectively mine the relationship within massive heterogeneous multidimensional data, complete unlabeled data classification, and provide data support for various model analysis of big data. Common big data density clustering methods are time-consuming and easy to cause errors in data density allocation, which affects the accuracy of data clustering. Therefore we propose a novel large data density peak clustering based on sparse auto-encoder and data space meshing via evidence probability distribution. Firstly, the sparse auto-encoder in deep learning is used to achieve feature extraction and dimensionality reduction for input high-dimensional data matrix through training. Secondly, the data space is meshed to reduce the calculation of the distance between the sample data points. When calculating the local density, not only the density value of the grid itself, but also the density value of the nearest neighbors are considered, which reduces the influence of the subjective selection truncation distance on the clustering results and improves the clustering accuracy. The grid density threshold is set to ensure the stability of the clustering results. Using the K-nearest neighbor information of the sample points, the transfer probability distribution strategy and evidence probability distribution strategy are proposed to optimize the distribution of the remaining sample points, so as to avoid the joint error of distribution. The experimental results show that the proposed algorithm has higher clustering accuracy and better clustering performance than other advanced clustering algorithms on artificial and real data sets.

Proton transfer driven by the fluctuation of water molecules in chitin film.

Proton-transfer mechanisms and hydration states were investigated in chitin films possessing the functionality of fuel-cell electrolytes. The absolute hydration number per chitin molecule (N) as a function of relative humidity (RH) was determined from the OH stretching bands of H2O molecules, and the proton conductivity was found to enhance above N = 2 (80%RH). The FIR spectrum at 500-900cm-1 for 20%RH (N < 1) together with first-principles calculations clearly shows that the w1 site has the same hydration strength as the w2 site. The molecular dynamics simulations for N = 2 demonstrate that H2O molecules with tiny fluctuations are localized on w1 and w2, and the hydrogen-bond (HB) network is formed via the CH2OH group of chitin molecules. Shrinkage of the O-O distance (dOO), which synchronizes with the barrier height, is required for proton transfer from H3O+ to adjacent CH2OH groups or H2O molecules. Nevertheless, dOO is hardly modulated for N = 2 because H2O molecules are strongly constrained on w1 and w2, and therefore, the transfer probability becomes small. For N = 3, novel HBs emerged between the additional H2O molecules broadly distributed on the w3 site and H2O molecules on w1 and w2. The transfer probability is enhanced because large fluctuations and diffusions in the whole H2O molecule yield large modulations of dOO. Consequently, long-range proton hopping is driven by the Zundel-type protonated hydrates in the water network.

Cost-effectiveness Analysis of Prophylaxis Versus On-demand Treatment for Children With Moderate or Severe Hemophilia A in China

BackgroundIt is still being determined if prophylaxis (PR) has superior cost effectiveness compared with on-demand (OD) treatment for moderate or severe hemophilia A (HA) children in China. Objective/PurposeTo evaluate the cost-effectiveness of PR and OD treatment for children with moderate or severe HA without inhibitors in China. MethodsA retrospective cost-effectiveness study was conducted on 640 HA children (373 and 267 children were on the PR and OD treatment, respectively) from January 2021 to November 2022. The Markov model was used to estimate the economic and clinical outcomes and would run for 17 yearly cycles with the initial age at 2 years. The transfer probabilities were extracted from the data of “Hemophilia Home Care Center” and the literature published. All patients’ drug costs were collected from the data of “Hemophilia Home Care Center”. One-way and probabilistic sensitivity analyses were conducted on the data to evaluate the robustness of the results. Results/FindingsPR was consistently associated with higher overall quality-adjusted life years (QALYs) compared with OD treatment (9.59 QALYs vs. 6.85 QALYs). The incremental cost-effectiveness ratio (ICER) of PR compared with the OD treatment was calculated to be approximately US$12,151.35 (RMB¥81,778.55) per QALY gained. This amount was lower than the willingness-to-pay (WTP) threshold of US$38,212.74 (RMB¥257,171.71). One-way sensitivity analysis found that the results were sensitive to the cost of OD and PR treatments. Conclusions/ImplicationsThis study indicated that PR is cost-effective compared with OD treatment for children with moderate or severe HA without inhibitors in China.

Study of nucleus orientation effect in the 238U+238U multinucleon transfer reaction

The improved quantum molecular dynamics model was employed to study the 238U+238U multinucleon transfer reaction at E c.m. = 833 MeV in this work. The influence of the orientation effect on the nuclear deformation at the contact moment, the composite system lifetime, the transfer rate and the fragment production mechanism are investigated. Statistical analysis reveals that at the contact moment, the orientation of the projectile and the target have less influence on each other and retain their respective initial characteristics to some extent. For collision parameters less than 6 fm, significant differences are observed in composite system lifetime and transfer rate under different orientation configurations; however, the orientation effect gradually diminishes with increasing collision parameters. Additionally, it is found that transfer reactions dominate when the collision parameter b ≤ 6 fm, while elastic and inelastic scattering events increase rapidly as the collision parameter exceeds 6 fm. Within the range of 10 ≤ b ≤ 13 fm, the transfer probability for side–side collisions is significantly higher compared to other cases.

Adaptive interacting multiple model particle filter based on a dual-pattern bat algorithm for maneuvering target tracking

The interacting multiple model particle filter (IMM-PF) is a filtering method commonly used for nonlinear non-Gaussian radar systems. Its transfer probabilities are usually fixed and dependent on prior information. When tracking maneuvering targets, the IMM-PF may cause excessive tracking errors due to the model switching lag. In addition, the particle impoverishment caused by the resampling of the particle filter seriously affects the filtering accuracy. Therefore, the IMM-PF has difficulty meeting the accuracy and speed requirements of modern high-performance radar target tracking systems. To address these problems, an adaptive interacting multiple model particle filter based on the dual-pattern bat algorithm (ADIMM-DPBA-PF) is proposed for tracking maneuvering targets under nonlinear and non-Gaussian conditions. First, the adaptive model switching mechanism is established to adjust the transition probabilities using the model probability posterior information at consecutive times, improving the model switching efficiency. Second, a particle filter based on the dual-pattern bat algorithm (DPBA-PF) is proposed. The filter exploits global search and local search strategies to optimize the particles and intelligently move the particles to the high likelihood region. Finally, a particle filter based on the dual-pattern bat algorithm is used to form the adaptive interacting multiple model method. The experimental results show that the proposed algorithm has better comprehensive performance than the IMM-PF.

Response Mechanism and Evolution Trend of Carbon Effect in the Farmland Ecosystem of the Middle and Lower Reaches of the Yangtze River

The farmland system in the global terrestrial ecosystem has dual attributes as both a carbon source and a carbon sink, playing a crucial role in controlling carbon emissions and mitigating global warming. Using carbon source and sink accounting of farmland ecosystems, we applied methods such as standard deviation ellipse, Tapio decoupling theory, and Markov chain to analyze the spatiotemporal changes, response mechanisms, and evolutionary trends of regional carbon effects. The results indicated that from 2011 to 2021, the farmland ecosystem in the middle and lower reaches of the Yangtze River consistently acted as a carbon sink. However, the net carbon sink showed slight fluctuations and significant spatial differences. The migration range of the net carbon sink center in the farmland ecosystem of the middle and lower reaches of the Yangtze River was relatively small, ranging from 115.52 to 115.77° E and 30.14 to 30.27° N. The decomposition of the Tapio decoupling index between the net carbon sink of the farmland ecosystem and agricultural output value showed the order of effects on their coupling relationship as follows: agricultural mechanization level &gt; agricultural mechanization efficiency &gt; agricultural output value &gt; planting scale. The probability of maintaining the original state of net carbon sink in various cities in the middle and lower reaches of the Yangtze River (over 77%) was much higher than the probability of transfer, making it difficult to achieve a leapfrog growth in net carbon sink. The net carbon sink at the city scale exhibits the Matthew effect and spatial spillover effect. The above research results clarify the spatiotemporal changes in carbon effects in agricultural production at multiple levels, including city, province, and region. They also provide a theoretical basis for formulating differentiated regional emission reduction and sink enhancement strategies in the middle and lower reaches of the Yangtze River, promoting the rapid development of low-carbon agriculture in China.

Impact of temperatures on grassland transfer strategy: Evidence from the Qinghai-Tibet plateau in China

The increasing severity and complexity climate change present significant challenges to agricultural and social activities. This study investigates the effects of extreme temperatures on grassland transfer strategies, utilizing micro-level survey datasets obtained from pastoral areas of the Qinghai-Tibet Plateau in China, which are then matched with weather data. Employing the piecewise linear method and the temperature bins approach, the results reveal that extreme high temperature weather has a positive and significant effect on grassland transfer decisions. Specifically, each additional day of high-temperature weather throughout the year increases the overall probability of grassland transfer by 0.52%. Additionally, the analysis reveals a U-shaped relationship between temperature and grassland transfer-in decision, with the turning point occurring in the of 5–8 °C range. Furthermore, the findings indicate that grassland transfer decisions are predominantly influenced by moderating effects of factors such as the grassland quality and non-pastoral income. Moreover, herder households characterized by high income levels and younger demographics exhibit a notable capability in rapidly applying adaptive strategies to cope with temperature variations. Projections for future climate change scenarios indicate that extreme temperatures will significantly negatively impact grassland transfer-out, while positively influencing transfer-in.

Blue and white light emissions and energy transfer in Tm3+ and Dy3+/Tm3+ doped lithium-aluminum-zinc phosphate glasses

Lithium-aluminum-zinc phosphate glasses doped with thulium and dysprosium ions are characterized by using spectroscopy techniques. The Judd-Ofelt parameters were evaluated to calculate the radiative parameters of thulium 1D23F4 and 1G43H6 visible transitions and 3H43F4 near infrared transition, which shows the highest optical amplification parameters. Upon 356 nm excitation, the Tm3+ doped phosphate glass emits blue light with CIE1931 chromaticity coordinates x = 0.1540 and y = 0.0283 and color purity around 96.8%. With excitations at 347 and 350 nm, the Dy3+/Tm3+ doped phosphate glass emits neutral and cold white light with correlated color temperature values of 4716 and 6628 K, respectively. The dysprosium emission decay time in the codoped glass is shorter than that in the Dy3+ single-doped glass, indicating a non-radiative energy transfer from Dy3+ to Tm3+. The dominant electrical interaction involved in the energy transfer, following the model Inokuti-Hirayama, is of dipole-dipole type. The energy transfer probability and efficiency are 769.0 s-1 and 0.53, respectively. Tm3+ doped and Dy3+/Tm3+co-doped lithium-aluminum-zinc phosphate glasses could be appropriate for blue and neutral/cold white light-emitting device applications.

Carbon metabolism mechanisms and evolution characteristics analysis of the food-water-energy nexus system under blue-green infrastructure changes

Food, water, and energy comprise a complex system (FWE nexus) that generates much carbon emissions during operation. At the same time, urban blue-green infrastructure (BGI) has a critical carbon sequestration function. This paper combines the functions of the FWE nexus and BGI and uses ecological network analysis (ENA) and the Markov model to measure the carbon metabolism (CM) mechanisms and evolutionary characteristics of BGI and FWE nexus (BGI-FWE nexus) complex systems. The results show that Guangzhou has high carbon emissions, and Zhaoqing and Huizhou have high carbon sequestration. Resident land and industrial and transportation land transfers to different land uses are more likely to produce positive carbon flows, while BGI transfers to other types are more likely to produce negative carbon flows. The study of CM mechanisms reveals a high proportion of competition relationships and a low proportion of mutualism relationships. The ecological utility index (EUI) tends to fall initially and then increase, peaking at 0.84 in 2015–2020, the highest value for the study period. The CM network has less system robustness (SR) and is in an unsustainable state of high redundancy and low efficiency. The mechanism evolution characterization study’s findings show a decreased likelihood of remaining original and less stability in the spatial transfer probability matrices of EUI and SR. In this study, we constructed a BGI-FWE nexus research framework based on the different CM functions of BGI and FWE nexus. The research framework contributes to the realization of carbon reduction in the FWE nexus system and is essential for the planning and management of urban BGI.

Research on the relationship of coupling coordination between digitalization and green development

The coupling coordination of digitalization and green development has become an inevitable requirement for building a new development pattern and achieving high-quality development of China’s economy. Based on the panel data of 284 Chinese cities from 2011 to 2021, this study uses the coupling coordination degree model, Dagum Gini coefficient, spatial convergence, Markov transfer probability matrix, and panel Tobit model to quantitatively analyze the spatial–temporal evolution characteristics and influencing factors of the coupling coordination degree of digitization and green development. The study results show that the coupling coordination degree (CCD) of digitalization and green development has an overall increasing trend during the study period, and the eastern region is higher than other regions which showing spatial non-equilibrium characteristics. The spatial difference of the CCD continues to downward, and inter-regional differences are the main source of the CCD. In the long run, there is a “catch-up effect” between cities with low CCD and those with high CCD, and the CCD of digitalization and green development will tend to a steady state. The transfer type of the CCD has the characteristics of “club convergence”, the probability of maintaining the original state is high, and the overall CCD shows a good development trend in the future. Factors such as environmental regulation, green innovation, industrial structure upgrading and financial efficiency can significantly contribute to the CCD of digitalization and green development. The above findings provide empirical evidence of the CCD of digitalization and green development to achieve high-quality economic development.

An Improved Ant Colony Algorithm with Deep Reinforcement Learning for the Robust Multiobjective AGV Routing Problem in Assembly Workshops

Vehicle routing problems (VRPs) are challenging problems. Many variants of the VRP have been proposed. However, few studies on VRP have combined robustness and just-in-time (JIT) requirements with uncertainty. To solve the problem, this paper proposes the just-in-time-based robust multiobjective vehicle routing problem with time windows (JIT-RMOVRPTW) for the assembly workshop. Based on the conflict between uncertain time and JIT requirements, a JIT strategy was proposed. To measure the robustness of the solution, a metric was designed as the objective. Afterwards, a two-stage nondominated sorting ant colony algorithm with deep reinforcement learning (NSACOWDRL) was proposed. In stage I, ACO combines with NSGA-III to obtain the Pareto frontier. Based on the model, a pheromone update strategy and a transfer probability formula were designed. DDQN was introduced as a local search algorithm which trains networks through Pareto solutions to participate in probabilistic selection and nondominated sorting. In stage II, the Pareto frontier was quantified in feasibility by Monte Carlo simulation, and tested by diversity-robust selection based on uniformly distributed weights in the solution space to select robust Pareto solutions that take diversity into account. The effectiveness of NSACOWDRL was demonstrated through comparative experiments with other algorithms on instances. The impact of JIT strategy is analyzed and the effect of networks on the NSACOWDRL is further discussed.

Vacancy transfer probability parameters: Database and a new empirical value for elements in the atomic number range 16 ≤ Z ≤ 92

In the present work, we offer a collection of documented values for vacancy transfer probabilities (ηXY, X=K,Li;Y=L,M,N,Li,Mj,Np,Op;i=1,2,3;j=1,2,3,4,5;p=1,4,5) sourced from published technical literature spanning 1993 to 2023 for elements in the atomic range 16≤Z≤92. We found 1200 experimental vacancy transfer probability values from 68 scientific papers, during the specified period. These have been compiled and summarized in tables, encompassing various parameters and elements. This data is also comprehensively analysed, including tables that display weighted average vacancy transfer probability (ηXY)W values along with the combined standard deviation and the average z-score. We also recommend a new collection of empirical values for the atomic parameters ηKL(T),ηKL2(R),ηKL3(R),andηKM(R) of vacancy transfer probabilities. This compilation offers an overview of the present state of atomic data for vacancy transfer probabilities. It is a valuable resource to guide future experimental and theoretical studies in this area.

NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment

To improve the performance of intelligent products and reasonably distribute the load of the loading robot, a multi-objective, and multi-objective (Traveling-salesman-problem, TSP) mathematical model was established. A genetic algorithm based on speed invariant and the elite algorithm is proposed to solve the multi-TSP assignment problem. To ensure the integration of the population, a population resettlement strategy with elite lakes was proposed to improve the probability of population transfer to the best Pareto solution. The experiment verified that this strategy can approach the optimal solution more closely during the population convergence process, and compared it with traditional Multi TSP algorithms and single function multi-objective Multi TSP algorithms. By comparing the total distance and maximum deviation of multiple robot systems, it is proven that this algorithm can effectively balance the path length of each robot in task allocation. From the research results, it can be seen that in genetic algorithms, resetting the population after reaching precocity can maintain the optimization characteristics of the population and have a high probability of obtaining Pareto solutions. At the same time, storing elite individuals from previous convergent populations for optimization can better obtain Pareto solutions.

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