Construction Of Prediction Model Research Articles

The construction, validation and promotion of the nomogram prognosis prediction model of UCEC, and the experimental verification of the expression and knockdown of the key gene GPX4

BackgroundAdequate prognostic prediction of Uterine Corpus Endometrial Carcinoma (UCEC) is crucial for informing clinical decision-making. However, there is a scarcity of research on the utilization of a nomogram prognostic evaluation model that incorporates pyroptosis-related genes (PRGs) in UCEC. MethodsBy analyzing data from UCEC patients in the TCGA database, four PRGs associated with prognosis were identified. Subsequently, a “risk score” was developed using these four PRGs and LASSO. Ordinary and web-based dynamic nomogram prognosis prediction models were constructed. The discrimination, calibration, clinical benefit, and promotional value of the selected GPX4 were validated. The expression level of GPX4 in UCEC cell lines was subsequently verified. The effects of GPX4 knock-down on the malignant biological behavior of UCEC cells were assessed. ResultsFour key PRGs and a “risk score” were identified, with the “risk score” calculated as (−0.4323) * GPX4 + (0.2385) * GSDME + (0.0525) * NLRP2 + (−0.3299) * NOD2. The nomogram prognosis prediction model, incorporating the “risk score,” “age,” and “FIGO stage,” demonstrated moderate predictive performance (AUC >0.7), good calibration, and clinical significance for 1, 3, and 5-year survival. The web-based dynamic nomogram demonstrated significant promotional value (https://shibaolu.shinyapps.io/DynamicNomogramForUCEC/). UCEC cells exhibited abnormally elevated expression of GPX4, and the knockdown of GPX4 effectively suppressed malignant biological activities, including proliferation and migration, while inducing apoptosis. The findings from tumorigenic experiments conducted on nude mice further validated the results obtained from cellular experiments. ConclusionFollowing validation, the nomogram prognosis prediction model, which relies on four pivotal PRGs, demonstrated a high degree of accuracy in forecasting the precise probability of prognosis for patients with UCEC. Additionally, the web-based dynamic nomogram exhibited considerable potential for promotion. Notably, the key gene GPX4 exhibited characteristics of a potential oncogene in UCEC, as it facilitated malignant biological behavior and impeded apoptosis.

Construction of brain metastasis prediction model in limited stage small cell lung cancer patients without prophylactic cranial irradiation.

Small cell lung cancer (SCLC) is a highly aggressive lung cancer variant known for its elevated risk of brain metastases (BM). While earlier meta-analyses supported the use of prophylactic cranial irradiation (PCI) to reduce BM incidence and enhance overall survival, modern MRI capabilities raise questions about PCI's universal benefit for limited-stage SCLC (LS-SCLC) patients. As a response, we have created a predictive model for BM, aiming to identify low-risk individuals who may not require PCI. A total of 194 LS-SCLC patients without PCI treated between 2009 and 2021 were included. We conducted both univariate and multivariate analyses to pinpoint the factors associated with the development of BM. A nomogram for predicting the 2- and 3-year probabilities of BM was then constructed. Univariate and multivariate analyses revealed several significant independent risk factors for the development of BM. These factors include TNM stage, the number of chemotherapy (ChT) cycles, Ki-67 expression level, pretreatment serum lactate dehydrogenase (LDH) levels, and haemoglobin (HGB) levels. These findings underscore their respective roles as independent predictors of BM. Based on the results of the final multivariable analysis, a nomogram model was created. In the training cohort, the nomogram yielded an area under the receiver operating characteristic curve (AUC) of 0.870 at 2 years and 0.828 at 3 years. In the validation cohort, the AUC values were 0.897 at 2 years and 0.789 at 3 years. The calibration curve demonstrated good agreement between the predicted and observed probabilities of BM. A novel nomogram has been developed to forecast the likelihood of BM in patients diagnosed with LS-SCLC. This tool holds the potential to assist healthcare professionals in formulating more informed and tailored treatment plans.

Construction and validation of a financial risk prediction model for e-commerce enterprises oriented to financial sharing

Abstract Relying on advanced technology, the financial shared management model realizes a breakthrough for the enhancement of enterprise financial management capability, and provides effective support for enterprise decision makers while mining financial risks. In this paper, the support vector machine algorithm (SVM) is used to construct a financial risk prediction model for e-commerce enterprises, and the radial basis kernel function (RBF) is selected among various kernel functions to enhance the discriminative ability of SVM. At the same time, the particle swarm algorithm (PSO) is introduced to optimize parameter γ and penalty factor C of the RBF kernel function, and the PSO-SVM fusion model is constructed, so as to improve the prediction accuracy of the e-commerce enterprise financial risk prediction model. After the experimental analysis of this paper on the various capabilities of enterprise financial management, it is found that the operating cash flow ratio of the e-commerce enterprise is less than 40% in 2017-2021, and reaches a minimum of 3% in 2020, which indicates that the Z e-commerce enterprise has insufficient ability to raise debt to operate, which may cause problems such as a decrease in the rate of return on assets. In addition, the cash flow of the e-commerce enterprise is highly variable, and the cash outflow from operating activities is significantly higher than the cash inflow in 2020, resulting in a negative net inflow of −111,451,575,000 yuan. It indicates that the Z e-commerce enterprise has a low degree of control over cash flow, and there is a financial risk of a sudden break in the capital chain. Therefore, the risk prediction model constructed in this paper can uncover potential hidden financial dangers and risks by analyzing the financial data of e-commerce enterprises.

Construction of a novel lower-extremity peripheral artery disease subtype prediction model using unsupervised machine learning and neutrophil-related biomarkers

Lower-extremity peripheral artery disease (LE-PAD) is a prevalent circulatory disorder with risks of critical limb ischemia and amputation. This study aimed to develop a prediction model for a novel LE-PAD subtype to predict the severity of the disease and guide personalized interventions. Additionally, LE-PAD pathogenesis involves altered immune microenvironment, we examined the immune differences to elucidate LE-PAD pathogenesis. A total of 460 patients with LE-PAD were enrolled and clustered using unsupervised machine learning algorithms (UMLAs). Logistic regression analyses were performed to screen and identify predictive factors for the novel subtype of LE-PAD and a prediction model was built. We performed a comparative analysis regarding neutrophil levels in different subgroups of patients and an immune cell infiltration analysis to explore the associations between neutrophil levels and LE-PAD. Through hematoxylin and eosin (H&E) staining of lower-extremity arteries, neutrophil infiltration in patients with and without LE-PAD was compared. We found that UMLAs can helped in constructing a prediction model for patients with novel LE-PAD subtypes which enabled risk stratification for patients with LE-PAD using routinely available clinical data to assist clinical decision-making and improve personalized management for patients with LE-PAD. Additionally, the results indicated the critical role of neutrophil infiltration in LE-PAD pathogenesis.

Construction of a nomogram risk prediction model for prolonged mechanical ventilation in patients following surgery for acute type A aortic dissection.

This study aims to analyze the risk factors associated with prolonged mechanical ventilation (PMV) in patients following surgical treatment for acute type A aortic dissection (ATAAD). The objectives include constructing a predictive model for risk assessment and validating its predictive efficacy. A total of 452 patients diagnosed with ATAAD and undergoing surgical procedures at a tertiary hospital in Nanjing between January 2021 and April 2023 were selected using a convenience sampling method. Patients were categorized into two groups: PMV group (n = 132) and non-PMV group (n = 320) based on the occurrence of prolonged mechanical ventilation (PMV), and their clinical data were compared. The data were randomly divided into a modeling set and a validation set in a 7:3 ratio. Risk factors for PMV were identified in the modeling group using logistic regression analysis. A risk prediction model was constructed using R 4.1.3 software, visualized via a column chart. Receiver Operating Characteristic (ROC) curves were generated using the validation set to assess model differentiation. Calibration curves were plotted to evaluate accuracy and consistency, and Decision Curve Analysis (DCA) was applied to evaluate clinical utility. The logistic regression analysis identified age, body mass index, preoperative white blood cell count, preoperative creatinine, preoperative cerebral hypoperfusion, and cardiopulmonary bypass time as significant risk factors for postoperative PMV in patients with ATAAD. The area under the curve (AUC) for the validation set ROC curve was 0.856, 95% confidence interval (0.805-0.907), indicating good discrimination. Calibration curves revealed strong alignment with the ideal curve, and the Hosmer-Lemeshow goodness-of-fit test indicated a well-fitted model (P = 0.892). The DCA curve demonstrated a high net benefit value, highlighting the model's strong clinical utility. The risk prediction model developed in this study for PMV in patients undergoing surgery for ATAAD exhibits robust predictive performance. It provides valuable insights for healthcare practitioners in predicting the likelihood of PMV and devising timely and personalized intervention strategies.

Risk factors for benign paroxysmal positional vertigo and construction of a nomogram predictive model.

To analyze the risk factors for benign paroxysmal positional vertigo (BPPV) and to construct a predictive nomogram model. In this retrospective study, 312 participants were enrolled, including 164 BPPV patients and 148 healthy subjects without BPPV. Risk predictors for BPPV were identified using univariate and multivariate analyses, and a clinical nomogram was constructed. The predictive accuracy was assessed by unadjusted concordance index (C-index) and calibration plot. Univariate and multivariate regression analysis identified stroke (95% CI, 0.575-5.954; P=0.022), hyperlipidemia (95% CI, 0.471-4.647; P=0.003), chronic suppurative otitis media (95% CI, 1.222-45.528; P=0.005), cervical spondylosis (95% CI, 1.232-3.017; P=0.005), and osteoporosis (95% CI, 1.130-3.071; P=0.001) were the independent risk factors for BPPV. These risk factors were used to construct a clinical predictive nomogram. The regression equation was: logit (P) = -6.820 + 0.450 * stroke + hyperlipidemia * 0.312 + chronic suppurative otitis media * 0.499 + cervical spondylosis * 0.916 + osteoporosis * 0.628. The calibration curves demonstrated excellent accuracy of the predictive nomogram. Decision curve analysis showed that the predictive model is clinically applicable when the threshold probability was between 20% and 60%. Stroke, hyperlipidemia, chronic suppurative otitis media, cervical spondylosis and osteoporosis are independent risk predictors for BPPV. The developed nomogram is useful in predicting the risk of BPPV.

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Risk factors influencing postoperative outcome of arthroscopic rotator cuff repair and construction of a nomogram prediction model.

To investigate the risk factors influencing the postoperative outcome of arthroscopic rotator cuff repair (ARCR) and develop a nomogram prediction model. A retrospective study was conducted on 302 patients who underwent ARCR from January 2019 to August 2023. Patients were categorized into two groups: a control group with 150 patients showing good recovery and an observation group with 152 patients exhibiting poor recovery. Relevant clinical data were collected and statistically analyzed. A nomogram model was constructed based on the results of multivariate logistic regression analysis. The model's accuracy, discrimination, and clinical utility were evaluated using calibration charts, AUC, c-index, and decision curve analysis. Internal validation was performed through self-random sampling. Univariate and multivariate regression analysis identified having a frozen shoulder, large rotator cuff tear, increased intraoperative rivet use, diabetes, and traumatic tear as predictive risk factors for poor postoperative outcomes. These factors were utilized to develop a clinical predictive nomogram. The nomogram model demonstrated excellent predictive accuracy for poor postoperative outcomes, both internally and externally. The unadjusted concordance index (C-index) was 0.793 [95% confidence interval (CI), 0.825-0.995]. The AUC for the nomogram was 0.788. Decision curve analysis revealed that the predictive model was clinically useful when the threshold probability ranged from 20 to 60%. The presence of a frozen shoulder, large rotator cuff tear, increased intraoperative rivet use, diabetes, and traumatic tear elevate the risk of suboptimal outcomes following ARCR. Conversely, having a higher preoperative University of California at Los Angeles Shoulder Rating Scale score mitigates this risk. This study introduces a novel nomogram model, exhibiting relatively high accuracy, which enables clinicians to precisely assess the postoperative adverse risk among patients with rotator cuff injuries requiring arthroscopic repair at the outset of treatment.

Construction and comparison of short-term prognosis prediction model based on machine learning in acute ischemic stroke

ObjectiveTo construct and compared the short-term prognosis prediction models of acute ischemic stroke (AIS) by machine learning (ML). MethodsRetrospectively study. The group W (mRS≤3) was clustered, and combined with group P (mRS>3) to form the post-clustering dataset for modeling. The “glmnet”, “rpart”, “xgboost”, “randomForest”, “neuralnet” packages were used to construct ML models. The accuracy, sensitivity, specificity, positive predict value (PPV), negative predict value (NPV) among the models were compared. Four external clinical datasets were used for external clinical validation. The optimal prediction model was determined by variable screening ability, model visualization, and external clinical validation performance. ResultsThe post-clustering dataset contains 139 patients (group W) and 122 patients (group P). The neutrophil multiplied by D-dimer (NDM) has predictive value in all ML prediction models in this study. In the decision tree model, NDMQ occupies the first tree node, When NDM≤5.62 and the age<74.5, the probability of poor prognosis of AIS is less than 20 %. When NDM>5.62 and accompanied by pneumonia, the incidence of poor prognosis of AIS is about 90 %. In the Random Forest (RF) model, NDMQ had the highest Gini index. The variable combination screened by the RF model had the best performance in the neural network, and the accuracy, sensitivity, specificity, PPV, and NPV of the external validation were 0.800, 0.774, 0.833, 0.857, and 0.741, respectively. The RF model had the best performance in the external clinical validation datasets, with accuracies of 0.646, 0.697, 0.695, and 0.713, respectively. ConclusionsNDM shows predictive value for AIS short-term prognosis in all ML models in this study. The optimal model in screening characteristic variables and the performance of in external clinical datasets was RF model. In the analysis of medical data with small sample size and outcome as categorical variables, RF could be used as the main algorithm to build a model.

Construction and validation of a risk prediction model for 3- and 5-year new-onset atrial fibrillation in HFpEF patients.

Patients with heart failure (HF) with preserved ejection fraction (HFpEF) are more prone to atrial fibrillation (AF) compared to those with heart failure with reduced ejection fraction (HFrEF). Nevertheless, a risk prediction model for new-onset atrial fibrillation (NOAF) in HFpEF patients remains a notable gap, especially with respect to imaging indicators. We retrospectively analyzed 402 HFpEF subjects reviewed at the Affiliated Hospital of Qingdao University from 2017 to 2023. Cox regression analysis was performed to screen predictors of NOAF. A nomogram was constructed based on these factors and internally validated through the bootstrap resampling method. A performance comparison between the nomogram and the mC2HEST score was performed. Out of the 402 participants, 62 (15%) developed atrial fibrillation. The risk factors for NOAF were finally screened out to include age, chronic obstructive pulmonary disease (COPD), hyperthyroidism, renal dysfunction, left atrial anterior-posterior diameter (LAD), and pulmonary artery systolic pressure (PASP), all of which were identified to create the nomogram. We calculated the bootstrap-corrected C-index (0.819, 95% CI: 0.762-0.870) and drew receiver operator characteristic (ROC) curves [3-year areas under curves (AUC) = 0.827, 5-year AUC = 0.825], calibration curves, and clinical decision curves to evaluate the discrimination, calibration, and clinical adaptability of the six-factor nomogram. Based on two cutoff values calculated by X-tile software, the moderate- and high-risk groups had more NOAF cases than the low-risk group (P < 0.0001). Our nomogram showed better 3- and 5-year NOAF predictive performance than the mC2HEST score estimated by the Integrated Discriminant Improvement Index (IDI) and the Net Reclassification Index (NRI) (P < 0.05). The nomogram combining clinical features with echocardiographic indices helps predict NOAF among HFpEF patients.

Development and validation of a nomogram for predicting 28-day mortality in critically ill patients with acute gastrointestinal injury: prospective observational study.

Developing and validating a clinical prediction nomogram of 28-day mortality in critically ill patients with acute gastrointestinal injury (AGI). Firstly, the construction of a clinical prediction model was developed using data obtained from a prospective observational study from May 2023 to April 2024. Then, data from a prospective multicenter observational study conducted in the intensive care units of 12 teaching hospitals in 2014 were utilized to independently and externally validate the clinical prediction model developed in the first part. We first screened the covariates of the development cohort by univariate cox regression, and then carried out cox regression analysis on the development cohort by backward stepwise regression to determine the optimal fitting model. Subsequently, a nomogram was derived from this model. A total of 1102 and 379 patients, 28-day mortality occurred in 20.3% and 15.8% of patients respectively, were included in the development and validation cohort, respectively. We developed a nomogram in critically ill patients with AGI and the AGI grade, APACHE II score, Mechanical ventilation (MV), Feeding intolerance (FI) and daily calorie intake (DCI) in 72 h, were independent predictors of 28-day mortality, with the OR of the AGI grade was 1.910 (95% CI, 1.588-2.298; P < 0.001), the OR of APACHE II score was 1.099 (95% CI, 1.069-1.130; P < 0.001), the OR of MV was 1.880 (95% CI, 1.215-2.911; P = 0.005), the OR of FI was 3.453 (95% CI, 2.414-4.939; P < 0.001) and the DCI > 0.7 or < 0.5 of calorie target is associated with increased 28-day mortality, with OR of 1.566 (95% CI, 1.024-2.395; P = 0.039) and 1.769 (95% CI, 1.170-2.674; P = 0.007), respectively. Independent external validation of the prediction model was performed. This model has good discrimination and calibration. The DCA and CIC also validated the good clinical utility of the nomogram. The prediction of 28-day mortality can be conveniently facilitated by the nomogram that integrates AGI grade, APACHE II score, MV, FI and DCI in 72 h in critically ill patients with AGI.

Construction of a risk prediction model based on the analysis of MRI imaging features for patients with triple-negative breast cancer and validation of its efficacy

Construction of a risk prediction model based on the analysis of MRI imaging features for patients with triple-negative breast cancer and validation of its efficacy

Construction of Prediction Model of Foodborne Disease Outbreaks and Its Trend Prediction - Guizhou Province, China, 2023-2025.

Foodborne diseases pose a significant public health concern globally. This study aims to analyze the correlation between disease prevalence and climatic conditions, forecast the pattern of foodborne disease outbreaks, and offer insights for effective prevention and control strategies and optimizing health resource allocation policies in Guizhou Province. This study utilized the χ2 test and four comprehensive prediction models to analyze foodborne disease outbreaks recorded in the Guizhou Foodborne Disease Outbreak system between 2012 and 2022. The best-performing model was chosen to forecast the trend of foodborne disease outbreaks in Guizhou Province, 2023-2025. Significant variations were observed in the incidence of foodborne disease outbreaks in Guizhou Province concerning various meteorological factors (all P≤0.05). Among all models, the SARIMA-ARIMAX combined model demonstrated the most accurate predictive performance (RMSE: Prophet model=67.645, SARIMA model=3.953, ARIMAX model=26.544, SARIMA-ARIMAX model=26.196; MAPE: Prophet model=42.357%, SARIMA model=37.740%, ARIMAX model=15.289%, SARIMA-ARIMAX model=13.961%). The analysis indicates that foodborne disease outbreaks in Guizhou Province demonstrate distinct seasonal patterns. It is recommended to concentrate prevention efforts during peak periods. The SARIMA-ARIMAX hybrid model enhances the precision of monthly forecasts for foodborne disease outbreaks, offering valuable insights for future prevention and control strategies.

Construction of prediction model of severe acute pancreatitis based on serum soluble T cell immunogloblulin and mucin domain-containing protein 3

To investigate the predictive value of the model based on soluble T cell immunogloblulin and mucin domain-containing protein 3 (sTIM3) for the progression of severe acute pancreatitis (SAP) in patients with acute pancreatitis (AP). A retrospective cohort study was conducted. The AP patients admitted to Changzhou First People's Hospital and Changzhou Second People's Hospital from June 1, 2020 to June 30, 2022 were enrolled. Mild AP (MAP) and moderately severe AP (MSAP) patients were classified as non-SAP group, and SAP patients were classified as SAP group according to the progression of AP patients during hospitalization. The basic data, blood biological indicators, serum sTIM3 level, bedside index for severity in acute pancreatitis (BISAP), acute physiology and chronic health evaluation II (APACHE II) score, modified computed tomography severity index (MCTSI) score within 48 hours of admission, and prognosis indicators were collected. Multivariate Logistic regression analysis was conducted to analyze the risk factors of the progression of SAP in patients with AP during hospitalization. Based on the results of multivariate analysis and the best parameters selected based on the minimal Akaike information criterion (AIC), the SAP prediction model based on sTIM3 was constructed. The receive operator characteristic curve (ROC curve) was plotted to analyze the predictive efficacy of the model. A total of 99 AP patients were enrolled, 80 patients in the non-SAP group and 19 patients in the SAP group. Compared with the non-SAP group, body mass index (BMI), drinking history ratio, heart rate (HR), respiration rate (RR), white blood cell count (WBC), red blood cell count (RBC), C-reactive protein (CRP), alanine aminotransferase (ALT), serum creatinine (SCr), procalcitonin (PCT), interleukin-6 (IL-6), sTIM3, BISAP score, APACHE II score and MCTSI score were significantly increased, and pulse oxygen saturation (SpO2), direct bilirubin (DBil) and IL-10 were significantly decreased. The length of intensive care unit (ICU) stay and total length of hospital stay of patients in the SAP group were significantly longer than those in the non-SAP group [length of ICU stay (days): 1.0 (0, 1.5) vs. 0 (0, 0), total length of hospital stay (days): 17.11±9.39 vs. 8.40±3.08, both P < 0.01]. Multivariate Logistic regression analysis showed that HR [odds ratio (OR) = 1.059, 95% confidence interval (95%CI) was 1.010-1.110, P = 0.017], DBil (OR = 0.981, 95%CI was 0.950-0.997, P = 0.043), and sTIM3 (OR = 1.002, 95%CI was 1.001-1.003, P = 0.027) were independent risk factors for predicting the progression of SAP in patients with AP, and the SAP prediction model based on sTIM3 was constructed: Logit(P) = -14.602+0.187×BMI+0.057×HR+0.006×CRP-0.020×DBil+0.002×sTIM3. ROC curve analysis showed that among the aforementioned single factor quantitative indicators, IL-6 was the most effective in predicting the progression of AP patients to SAP during hospitalization, but the predictive performance of prediction model based on the sTIM3 was significantly better than IL-6 [area under the ROC curve (AUC) and 95%CI: 0.957 (0.913-1.000) vs. 0.902 (0.845-0.958), P < 0.05]. The model based on serum sTIM3 demonstrated good predictive value for the progression of SAP in patients with AP.

Construction of prediction model for invasive ductal carcinoma derived from intraductal papillary mucinous neoplasm using time density curve

Construction of prediction model for invasive ductal carcinoma derived from intraductal papillary mucinous neoplasm using time density curve

Research on the Construction of Enterprise Compliance Management System in the Context of Deep Learning

Abstract Migration learning is a kind of deep learning that plays an important role in risk prediction and warning. In this paper, we use the Triplet-loss representation learning technique to map data samples of the same category to adjacent spatial regions, map data samples of different categories to different spatial regions, and add domain adaptive computation to complete domain adaptation. XGBoost performance is also optimized to improve the prediction results. After completing the risk prediction model construction, this paper also incorporates indicator weights to propose a risk warning model based on a weighted knowledge graph. The analysis found that the model accuracy of the compliance risk prediction model is 1.155% higher than that of two traditional prediction models on average, and the indicators of accuracy, F1-score, and AUC are all over 0.99, which indicates that it has high accuracy. After applying the optimized risk management system in an enterprise, it is found that the weight of the indicators of the enterprise’s compliance management system reaches 25.71%, the combined weight of the legal audit system and the risk prevention and control mechanism is 16.23% and 14.10%, respectively, and the combined weight of the two indicators of the appraisal and evaluation of the performance of the compliance management and the accountability for the violation of the law exceeds 8%. The weights of the indicators related to the degree of business standardization are close to 16.51%, indicating that the enterprise’s daily production and operation activities are standardized and orderly, and the perfection of the compliance management system is in line with the real situation of the enterprise. It can be seen that the risk management system makes accurate judgments on the compliance risk of the selected enterprise, which reflects the feasibility of the application of the new model.

Integration of Multi-Omics Data and Prognostic Prediction Model Construction Based on Deep Multi-View Contrastive Learning Methods

Integration of Multi-Omics Data and Prognostic Prediction Model Construction Based on Deep Multi-View Contrastive Learning Methods

Design and research on multi-sensory comfort data acquiring of tight sportswear in motion

This study aims to achieve intelligent monitoring of clothing comfort in motion, providing data and technical assistance for the research of comfort in motion and an efficient design foundation for sportswear comfort optimization. In view of the present situation of smart wear used in clothing comfort research, this paper primarily presents the construction of an acquisition system in motion, including temperature, humidity, and pressure data acquisition device, data pre-processing, data storage, and so on, which was called the ATHPD system for short. With the help of this acquisition system, tights in motion may now be tested for pressure, heat, and humidity for the first time. It can also transmit data wirelessly. Meanwhile, in order to verify the effectiveness of the acquisition system, by comparing the measurement data of the acquisition system (ATHPD system) with the measurement data of existing acquisition equipment (AMI3037 pressure measurement system, DS1923 button temperature and humidity recorder). The findings demonstrate that there is no significant difference between the data collected by the two methods, which proves the reliability of the acquisition system in this paper. The system guarantees the same quantity of temperature, humidity, and pressure data to be gathered at the same time and the consistency of the corresponding time points and also provides relevant data support for the evaluation and prediction model construction of human comfort in motion.

Data-driven marketing strategy and consumer behavior prediction model construction

Data-driven marketing strategy and consumer behavior prediction model construction

Prediction of wheat SPAD using integrated multispectral and support vector machines.

Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health and effective field management. Multispectral imagery obtained from unmanned aerial vehicles (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet fully considered the impact of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials from winter wheat natural populations in Xinjiang, collected between 2020 to 2022, were analyzed. UAV multispectral images were obtained in the experimental area, and vegetation indices were extracted to analyze the correlation between the selected vegetation indices and SPAD values. The input variables for the model were screened, and a support vector machine (SVM) model was constructed to estimate SPAD values during the heading, flowering, and filling stages under different water stresses. The aim was to provide a method for the rapid acquisition of winter wheat SPAD values. The results showed that the SPAD values under normal irrigation were higher than those under water restriction. Multiple vegetation indices were significantly correlated with SPAD values. In the prediction model construction of SPAD, the different models had high estimation accuracy under both normal irrigation and water limitation treatments, with correlation coefficients of predicted and measured values under normal irrigation in different environments the value of r from 0.59 to 0.81 and RMSE from 2.15 to 11.64, compared to RE from 0.10% to 1.00%; and under drought stress in different environments, correlation coefficients of predicted and measured values of r was 0.69-0.79, RMSE was 2.30-12.94, and RE was 0.10%-1.30%. This study demonstrated that the optimal combination of feature selection methods and machine learning algorithms can lead to a more accurate estimation of winter wheat SPAD values. In summary, the SVM model based on UAV multispectral images can rapidly and accurately estimate winter wheat SPAD value.