Statistical Prediction Models Research Articles

An Investigation in to Sports Analytics and Related Tools

This study explores the application of sports analytics and the utilization of various tools in the sports industry. Sports analytics, also known as sports data analytics, is the application of data analysis and statistical approaches to extract important insights. Additionally, it aids in making well-informed judgments and enhancing performance in the realm of sports. Sports data analytics include a diverse array of methods and approaches, such as statistical analysis, machine learning, data visualization, predictive modeling, and optimization algorithms. The objective of sports business analytics is to get practical insights from a dataset in order to enhance decision-making and foster innovation in the sports industry

P-646 Prognostic statistical model of age-specific anti-Müllerian hormone (AMH) decrease based on hormone values of 23,528 women

Abstract Study question The analysis of anti-Müllerian (AMH) levels, can a useful statistical model be constructed to predict the rate of decrease of AMH levels at different ages? Summary answer A determination of AMH levels can be an objective marker of a ovarian age and a prognostic marker of the rate of ovarian function decline. What is known already The anti-Müllerian hormone is a dimeric glycoprotein produced by granulosa cells of the antral follicles in the ovaries, and AMH participates in the formation of the dominant follicle during folliculogenesis. Serum AMH levels are relatively constant and show little variability between and within the menstrual cycle. The level of serum AMH is proportional to the number of antral follicles and therefore most suitable for determining ovarian reserve. For this reason, AMH is a convenient marker for the biological age of the ovary and for the response to controlled ovarian stimulation (COS). Study design, size, duration A nationwide cross-sectional retrospective study was conducted between 2017 and 2023 and included 23,528 women the ages of 20 - 50, in which AMH levels were measured once. We performed an analysis of the different AMH levels at different ages and constructed a predictive statistical model for the rate of AMH decline. Results were analyzed in subgroups with AMH values < 1 ng/mL, between 1 and 3,5 ng/mL, and > 3,5 ng/mL, at one-year intervals. Participants/materials, setting, methods All women who visited the laboratory for AMH testing were included in the analysis. The samples were collected in 38 points throughout the country and the analysis was carried out in one centralized laboratory using the Ansh Labs (Webster, TX, USA) manual assays, including the ultra-sensitive AMH enzyme-linked immunosorbent assay (ELISA), with a management range of 0,08 - 14,2 ng/ml. The intra-assay variations were 1,97% and 4,00%. The inter-assay variations were 4,63% and 1,98%, respectively. Main results and the role of chance When analyzing all samples in each consecutive year, we found that the rate of AMH decline was approximately linear and inversely proportional to the woman’s age. However, when analyzing the results for the rate of AMH reduction in the different subgroups, we found out that: In women with AMH < 1 ng/ml, the rate of decline with advancing age changes as follows: until age 34, the decline in AMH is very slow, between ages 35 and 45 there is a rapid acceleration of the process.. In women with AMH between 1 and 3,5 ng/ml, the rate of decline with age was approximately constant. In women with AMH > 3,5 ng/ml, the rate of hormone decline is approximately constant but the year-on-year decline rate is decreasing. Based on our analysis and clinical experience, it is our opinion that the rate of AMH decline for the low AMH group has a non-linear characteristic, while ingeneral different groups exhibit different dynamics for the rate of decline. On this basis, we created a statistical model to predict the decrease in AMH levels at different ages. Limitations, reasons for caution Women of only one ethnicity were included in the study. The studied group is heterogeneous, which does not exclude the inclusion of women with a certain pathology and infertility. In some cases, a single determination of AMH could not predict individual differences in rates of hormone reduction. Wider implications of the findings Determination of reference values for AMH at different ages, based on the large volume of examined samples may facilitate the application of AMH in clinical practice. Based on our statistical analysis, creating a prognostic model for decreasing ovarian function and time to menopause appears feasible. Trial registration number NA

Effect of Laser Parameters on Surface Texture of Polyformaldehyde and Parameter Optimization

This research aimed to investigate the influence of laser process parameters on the surface texture of Polyformaldehyde (POM) and to improve its processability and process predictability. A comparative experiment and analysis involving multiple processing parameters, including laser power, scanning speed, and pulse width, were conducted on POM. Statistical prediction models of laser processing POM were established among the laser power, scanning speed, pulse width, texture depth, surface roughness at the bottom of texture, and multi-objective optimization and experimental verification of process parameters were carried out based on the grey-Taguchi analysis method. Experimental results show that the laser power and scanning speed significantly affect the texture depth. Higher laser power and lower scanning speed are conducive to forming depth. The surface roughness at the bottom of the texture increases with the increase in scanning speed and shows a tendency to rise and then fall as the laser power increases. The surface roughness and texture depth obtained under the optimal process parameters(A5B1C1) are 1.373 μm and 466.891 μm, respectively, which were reduced by 10.08 % and increased by 3.42 % compared with the minimum surface roughness and maximum depth in the orthogonal experiments. The validation experiments of the prediction model show that it can meet the reliability requirements, and the errors of the predicted values of depth and surface roughness are 1.86 % and 7.60 %, respectively. The above research provides theoretical and experimental support for the precise control of surface texture prepared by laser processing POM.

An effective parametric sensitivity analysis to improve electrical and thermal energy/exergy efficiency of PVT system using nanofluid

A 3D steady-state model of a photovoltaic/Thermal system with nanofluid is studied to investigate the electrical and thermal performance of PVT by utilizing a combination of statistical modeling and numerical simulation. The 3D CAD (Computer Aided Design) modeling of the photovoltaic thermal (PV/T) is performed using Ansys Space Claim. The current study is based on using a 2-way coupling FSI approach for modeling nanofluid-based PVT systems. Five output responses electrical power, thermal power, electrical exergy, thermal exergy, and entropy generation are chosen against four input parameters, solar radiation (G_sun), ambient temperature (Tamb), mass flow rate (ṁ), and nanoparticles concentration % (φ) to analyze system performance. A statistical prediction model is developed using response surface methodology (RSM) that starts from the design of experiment (DOE). The predicted outcomes obtained from the statistical model and the numerical simulation of the heat transfer model exhibit the existence of good fitness. Furthermore, optimization is performed using RSM for various optimization objectives to suggest the ideal design parameter values for optimal system performance. Sensitivity analysis illustrates that a rise in ambient temperature causes a drop in electrical power production and leads to an increase in thermal power. However, the increase in solar radiation boosts electrical power and causes a drop in thermal exergy, entropy generation increases with an increase in solar irradiation. Similarly, an increase in mass flow rate leads to an increase in thermal power. Multi-objective optimization with the highest composite desirability values depicts the influence of input design parameters and has a favorable effect on the objective of concurrently maximizing electrical and thermal power.

Reliability framework for CLT floors in out-of-plane bending using Monte Carlo simulation

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index (β) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

IntroductionRescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.MethodsIn this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2) as performance metrics.ResultsIn this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA’s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions.DiscussionWhile the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

Prospective validation of clinical deterioration predictive models prior to intensive care unit transfer among patients admitted to acute care cardiology wards

Objective. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool developed before COVID-19 and demonstrate model performance during the COVID-19 pandemic. Approach. The analytic system (CoMETⓇ, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10 422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns. Main results. Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit, primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.725 to 0.737. Significance. The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic.

A generation-storage coordination dispatch strategy for power system based on causal reinforcement learning

In the backdrop of global energy transformation, power systems integrating high proportions of renewable energy sources are facing unprecedented challenges in operational stability and dispatch efficiency. To address these challenges, this study introduces a generation-storage coordination real-time dispatch strategy based on Causal Power System Dynamic Reinforcement Learning (CPSDRL). Diverging from traditional reinforcement learning approaches, CPSDRL innovatively incorporates causal inference within the state prediction model – the crux of model-based reinforcement learning – thereby establishing the Power Causal Dynamic Model (PCDM). Assisted by the prior knowledge of power systems, the model significantly enhances prediction accuracy and reliability through a two-stage training process. Utilizing PCDM, this study further applies a direct policy search algorithm to optimize the real-time dispatch strategy. Experimental results indicate that the proposed method improves the stability of generation-storage coordination real-time dispatch and exhibits competitive advantages in sample efficiency and computational speed, compared to traditional model-based and model-free reinforcement learning algorithms. This method is expected to enhance the practicality and adaptability of causal reinforcement learning techniques in power system scheduling and control.

Adsorptive removal of perfluorooctanoic acid from aqueous matrices using peanut husk-derived magnetic biochar: Statistical and artificial intelligence approaches, kinetics, isotherm, and thermodynamics

Removal of perfluorooctanoic acid (PFOA) from water matrices is crucial owing to its pervasiveness and adverse ecological and human health effects. This study investigates the adsorptive removal of PFOA using magnetic biochar (MBC) derived from FeCl3-treated peanut husk at different temperatures (300, 600, and 900 °C). Preliminary experiments demonstrated that MBC600 exhibited superior performance, with its characterization confirming the presence of γ-Fe2O3. However, efficient PFOA removal from water matrices depends on determining the optimum combination of inputs in the treatment approaches. Therefore, optimization and predictive modeling of the PFOA adsorption were investigated using the response surface methodology (RSM) and the artificial intelligence (AI) models, respectively. The central composite design (CCD) of RSM was employed as the design matrix. Further, three AI models, viz. artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were selected to predict PFOA adsorption. The RSM-CCD model applied to optimize three input process parameters, namely, adsorbent dose (100–400 mg/L), pH (3–10), and contact time (20–60 min), showed a statistically significant (p < 0.05) effect on PFOA removal. Maximum PFOA removal of about 98.3% was attained at the optimized conditions: adsorbent dose: 400 mg/L, pH: 3.4, and contact time: 60 min. Non-linear analysis showed PFOA adsorption was best fitted by pseudo-second-order kinetics (R2 = 0.9997). PFOA adsorption followed Freundlich isotherm (R2 = 0.9951) with a maximum adsorption capacity of ∼307 mg/g. Thermodynamics and spectroscopic analyses revealed that PFOA adsorption is a spontaneous, exothermic, and physical phenomenon, with electrostatic interaction, hydrophobic interaction, and hydrogen bonding governing the process. A comparative analysis of the statistical and AI models for PFOA adsorption demonstrated high R2 (>0.99) for RSM-CCD, ANN, and ANFIS. This research demonstrates the applicability of the statistical and AI models for efficient prediction of PFOA adsorption from water matrices using MBC (MBC600).

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models.

Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome. Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm. The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes. Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Prediction of hypertensive disorders in pregnant women in the «gray» risk zone following combined first-trimester screening

Introduction. In obstetrics, hypertensive disorders of pregnancy (HDP) including preeclampsia (РЕ) are one of the primary causes resulting in critical cases and maternal mortality. HDP prediction is a milestone that allows preventing complications as well as reducing the number of most common relevant complications of pregnancy. Existing algorithms that predict PE risk distribute the risks in such a way that a considerable number of patients fall into the category of false negative results, and, consequently, receive no timely prevention and proper follow-up. In particular, this cohort usually consists of patients with borderline high risks, who may be designated as a medium risk group or located in a “gray” zone. Aim: to develop a prognostic model for risk stratification in female patients with borderline to high developing PE risk based on combined first-trimester screening. Materials and Methods. A prospective comparative study included 1089 female patients who underwent a combined screening at 11–14 weeks of gestation. Group 1 consisted of female patients at high РЕ risk (1:100 and greater), while female patients at moderate risk (1:101–1:250) and low risk (below 1:250) were included into Group 2 and Group 3, respectively. All pregnant women underwent examination including assessed anamnestic, general clinical and laboratory data, mean blood pressure (BP), uterine artery pulsatility index, serum level of human chorionic gonadotropin beta-subunits (β-hCG), placental growth factor (PlGF), pregnancy-associated plasma protein-A (PAPP-A). Results. An impact of various factors on risk of developing hypertensive disorders in pregnancy was assessed by binary logistic regression by identifying most significant among them and generating a statistical prediction model – the prognostic index of hypertensive disorders in pregnancy. The latter included: obstetric history, body mass index, PlGF, mean ВР, and alanine aminotransferase level. The sensitivity and specificity comprised 91.2 and 53.6 %, respectively, and the method effectivenesswas 81.8 %. Conclusion. The method proposed for HDP prediction is a second-line approach that may be used in clinical practice to stratify patients with borderline high risk of developing PE.

Targeted Anthocyanin Enrichment of Cranberry Juice by Electrodialysis with Filtration Membranes: Impact of Filtration Membrane Physicochemical Properties and Predictive Statistical Models.

To optimize cranberry juice enrichment, correlation between physicochemical properties of filtration membranes (FM) and anthocyanin migration was investigated during electrodialysis with filtration membranes (EDFM) using redundancy (RDA) and multivariate regression (MRGA) analyses. Six polyether sulfone (PES) and polyvinylidene fluoride (PVDF) membranes with molecular weight cut-offs between 150 and 500 kDa, commercially available at large scale, were characterized in terms of nine physicochemical characteristics and used for EDFM. The highest migration of total anthocyanin was obtained with PVDF 250 kDa, with a global migration rate of 3.5 ± 0.4 g/m2·h. RDA showed that two FM properties (mesopore porosity and hydrophilic porosity) were significantly negatively correlated to the anthocyanin's migration and explained 67.4% of their total variation in migration. Predictive MRGA models were also developed for each anthocyanin based on these significant FM properties. A combination of intermolecular interactions may lead to binding in a cooperative and synergistic mode and hinder the anthocyanin migration.

Analytical prediction and numerical verification of stress concentration profile around an in-situ tow break in resin-impregnated filament-wound composites

A new empirical analytical approach is developed for predicting the stress concentration profile around an in-situ tow break in filament-wound composites. A shear-lag analysis is firstly performed to solve for the perturbational axial displacement of the broken tow and resultant debonding lengths. Solution of stress field caused by tangential load on the surface of an elastic half space is then utilized in combination with superposition principle to obtain the overload magnitudes in the neighboring tows. Subsequently, high-fidelity finite element analysis on a representative uni-directional laminate model under different stress states is performed, and excellent overall agreement is observed between analytical and numerical predictions. The proposed method takes into account essential aspects such as transversely isotropic material properties, in-situ stress states and their effect on the interfacial frictional forces in the debonded interfaces, and thus provides a convenient way to evaluate the stress concentration factors in damaged filament-wound composites. In addition, this approach can be applied to yield auxiliary failure evaluation criteria for statistical strength prediction or finite element modeling of filament-wound composites or similar structures.

Risk prediction strategies using intraoperative physiological data in adults undergoing surgery: a systematic review study protocol

Abstract Postoperative mortality accounts for 7.7% of all global deaths, while postoperative complications increase 1-year mortality by 60%. Risk prediction models for postoperative complications and mortality can facilitate tailored risk mitigation strategies. However, most models incorporate only preoperative patient-related factors as predictors and do not capture dynamic risks or intraoperative events. This systematic review seeks to evaluate the predictive capability of intraoperative physiology derived from routine anaesthetic monitoring and the feature extraction methods for these variables. This review will include both prospective and retrospective studies that incorporate intraoperative physiological measurements into the development, validation or updating of a statistical prediction model, to identify those at risk of major end-organ (cardiovascular, pulmonary, renal and neurological) complications and mortality up-to 90 days postoperatively. We will identify models developed in two settings: those undergoing cardiac surgery and heterogeneous adult patient cohorts undergoing non-cardiac surgery. The review will be reported according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This review will evaluate the available literature on intraoperative physiology as predictor variables, to improve feature extraction methods for risk prediction models development while understanding how to capitalize on the vast routinely collected intraoperative physiological data sets that are increasingly available. This review is registered on PROSPERO, registration number CRD42023474384.

Process Parameter Optimization of Directed Energy Deposited QT17‐4+ Steel

AbstractThe feasibility of using argon‐atomized QT 17‐4+ stainless steel powder for directed energy deposition (DED) additive manufacturing is studied. The QT 17‐4+ steel is a novel martensitic steel designed based on the compositional modification of the standard 17‐4 precipitation‐hardened (PH) stainless steel. This modification aims to achieve better mechanical properties of as‐deposited components compared to the heat‐treated wrought 17‐4PH steel. In this study, QT 17‐4+ steel powder is used for DED, for the first time. The influence of laser power, laser scan speed, powder feed rate, and hatch overlap on the density is studied. The central composite design is used to determine the experimental matrix of these factors. The response surface methodology is used to obtain the empirical statistical prediction model. Both columnar and equiaxed parent austenite grain structures are observed. X‐ray diffraction analyses reveal a decrease in the percentage of retained austenite from 19% in the powder to 5% after DED. The microhardness of the DED processed sample in the as‐deposited state is slightly higher than that of wrought 17‐4PH steel either solution‐annealed or H900‐aged. A higher 0.2% yield strength, a lower ultimate tensile strength, and lower elongation are observed for the vertically printed test sample, when compared to the horizontal one.

Trend and interannual variability of summer marine heatwaves in the tropical Indian ocean: Patterns, mixed layer heat budget, and seasonal prediction

Marine heatwaves (MHWs) are extreme sea surface temperature (SST) events in all ocean basins, with far-reaching impacts on marine ecosystems and socio-economy. The leading patterns, trend, and interannual variability of summer MHWs in the tropical Indian Ocean (TIO) are investigated in this study. The first empirical orthogonal function (EOF) mode of frequency of MHWs exhibits a monopole pattern over the entire basin. This mode is highly associated with the concurrent Indian Ocean Basin warming, indicating a remarkable trend over the past four decades. The linear trend in MHW properties largely relates to increased summer Indian Ocean mean SST. The second EOF mode exhibits a zonal dipole with the MHW numbers increasing in the west and decreasing in the east. On the interannual timescale, the first two EOF modes are remotely affected by antecedent and concurrent El Niño events, respectively. The ocean mixed layer budget is utilized for examining the formation of different summer MHW patterns. During the preceding spring, the surface heat flux is important for the development of MHWs, while the ocean advections play a secondary role in the South Indian Ocean for the MHW monopole. Once the SST anomaly rises in summer, the ocean advections play a dominant role in maintaining the SST. Last, we assess the prediction skill of summer TIO MHWs by performing a bilinear seasonal statistical prediction model. Our results suggest the frequency of summer MHWs in the TIO could be predicted one season in advance. This study has great implications for understanding and predicting ocean extreme events in the TIO.

Abstract PO5-03-03: Nomogram for predicting brain metastasis in early-stage breast cancer patients

Abstract Nomogram for predicting brain metastasis in early-stage breast cancer patients Background: Brain metastasis is a significant concern for patients with early-stage breast cancer, as it can have a detrimental impact on survival and quality of life. Clinical and pathological characteristics of the primary tumor may be modeled into a nomogram that can predict the risk of brain metastasis. The goal is to create a nomogram and clinical calculator to assist in selection of patients with high risk of brain metastasis to enrich patient population for accrual on clinical trials for the purpose of preventing or delaying emergence of brain metastasis, at an early stage of their disease. Methods: We reviewed all early-stage breast cancer patients (stages I to III) N=40290 from the University of Texas MD Anderson Cancer Center, Houston, TX, from 1/1/1997 to 5/8/2020. We utilized multiple predictive statistical models to identify the most relevant variables and optimal predictive criteria. Variables for the model included patient age, tumor characteristics (such as grade,hormone receptor and HER2 status) and medical treatment (such as chemotherapy and endocrine therapy). The Receiver Operating Characteristic (ROC), and its AUC (area under the curve) was used to demonstrate the performance of the multivariate model to predict the occurrence of brain metastasis at different time points (1 year, 2 years, and 5 years), distinguishing between patients who develop brain metastasis in a time-dependent manner and those who do not. Results: The results showed the following clinical variables to be statistically significant in predicting brain metastasis: younger age, high estrogen and progesterone receptor percentage, higher tumor size; use of aromatase inhibitor therapy, and use of selective estrogen receptor modulator therapy were associated with a decreased risk of brain metastasis. Higher grade, Ki-67 levels, HER2-positive status, lymphovascular invasion and the use of chemotherapy was associated with an increased risk. The AUC values for the prediction of brain metastasis at different time points are as follows: 0.85 at 1 year, 0.83 at 2 years, and 0.82 at 5 years. These values indicate that the multivariate model has good discriminative ability, with higher values indicating a better predictive performance. An AUC of 0.85 suggests that the model has a high probability of correctly distinguishing between patients who will develop brain metastasis and those who will not at 1 year. Conclusions: Our predictive tool holds promise in assisting clinicians when making informed, personalized care decisions for patients at risk of developing brain metastasis. The model will be validated using independent patient cohorts from Institut Jules Bordet, Brussel, Belgium, to assess their accuracy and reproducibility. Continued research and validation will further refine these models, increasing their reliability and their clinical/clinical research applicability. Citation Format: Akshara Singareeka Raghavendra, Nuria Kotecki, Kristofer Jennings, Ottavia Amato, Debu Tripathy, Ahmad Awada, Nuhad Ibrahim. Nomogram for predicting brain metastasis in early-stage breast cancer patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-03-03.

Rudarenje podataka za pomorsku sigurnost na temelju dubokog učenja: studija slučaja Južnog Vijetnama

High-speed passenger vessels, integrated river and sea vessels, container vessels, oil tankers, and other underwater vehicles operating in maritime traffic are among the types of vessels that must be equipped with AIS and VHF. The safety of navigation is one of the major problems in the maritime sector, particularly in Vietnam. Furthermore, marine traffic in the seaport zone is a common and difficult issue to manage in areas with a high volume of vessel traffic, mostly in places where the infrastructure supporting navigation is inadequately developed to meet the rapidly growing demands of the contemporary world. Therefore, it is necessary to create an integrated maritime management system to improve the efficiency of data exploitation and support maritime safety. To address this challenge, this study suggests a Maritime Traffic State Prediction (MTSP) model to predict traffic conditions in the channels where real-time data collection is insufficient in some specific locations. We recommend a deep learning method using Long Short-Term Memory (LSTM) networks to predict the safe path of the vessel in case of missing data segments. The findings have shown that the proposed approach encourages the mining of historical vessel data for maritime traffic, is ready to be applied, and can easily be implemented in a computer program or a web-based app.

Exploration of maintenance technology of aviation new energy power battery under machine learning

The existing prediction models on the market have uncertainties and errors, which cannot accurately predict the remaining life of batteries. These problems lead to excessive or delayed maintenance of batteries, thereby affecting their performance and safety. Therefore, machine learning-based maintenance technology for aviation new energy power batteries has become a new research direction. This study used machine learning algorithms to construct a battery status classification and prediction model by collecting a large amount of aviation new energy power battery data. Through experiments, the model significantly improved its accuracy in battery status recognition tasks, with a maximum of 96%. It can accurately identify tasks such as battery health status, lifespan prediction, and fault detection. The model had made progress in balancing accuracy and recall, with a high F1 value. This meant that the model could accurately identify the abnormal state of the battery while minimizing misjudgment of normal batteries as much as possible. These research results provided important support for battery maintenance in the aviation field, with the potential to improve battery availability, extend service life, and ensure flight safety while reducing maintenance costs.

A simulation-driven prediction model for state of charge estimation of electric vehicle lithium battery

Accurately predicting the state of charge (SOC) of lithium-ion batteries in electric vehicles is crucial for ensuring their stable operation. However, the component values related to SOC in the circuit typically require estimation through parameter identification. This paper proposes a three-stage method for estimating the SOC of lithium batteries in electric vehicles. Firstly, the parameters of the constructed second-order RC circuit are identified using the Forgetting Factor Recursive Least Squares (FFRLS) method. Secondly, an innovative approach is employed to construct a battery simulation model using modal-data fusion method. Finally, the predicted values of the simulation model are corrected using the unscented Kalman filter (UKF). Validation through datasets demonstrates the high precision of this method in parameter identification. Moreover, in the comparison of SOC prediction corrections with Particle Filter (PF), Extended Kalman Filter (EKF), and the proposed UKF on simulated prediction data and experimental test data. The proposed method achieves the lowest root mean square error (RMSE) of 0.0025 for simulation prediction data and 0.0186 for experimental test data. It also maintained its error within 5 % on actual data.