Regression Tree Model Research Articles

AI-powered sensor fault detection for cost-effective smart greenhouses

Sensor networks in greenhouses play a pivotal role in controlling the stability of environmental and chemical factors. The Internet of Things (IoT) has been widely adopted for monitoring various sensor networks in greenhouses. In the present study, an IoT platform for remote monitoring of greenhouse environment is designed. The platform consists of a sensor node, a sink node, and an edge server. The sensor node measures indoor and outdoor humidity and temperature, interior CO(g), and interior luminosity and transmits data to the sink node, where it is timestamped. The sink node is connected to an edge server through the Mosquitto MQTT broker and the data is subsequently transferred to a MongoDB Cloud infrastructure, where the data of each variable is stored in proper formats. In the second part of the study, four 1D convolutional neural networks (CNNs) were developed for data prediction of each sensor to provide fault-tolerance in the system. The first three models, for predicting inner humidity and temperature, outdoor temperature, and outdoor humidity, directly predict the actual data of the faulty sensor based on regression analysis. The last model is designed for predicting CO and luminosity and performs data classification for faulty sensors. The models provide a high level of precision in their predictions. The RMSE for interior temperature and humidity and exterior temperature and humidity are 0.86 ℃, 3.47%, 0.682 ℃, and 2.74%, respectively. Additionally, the accuracy for luminosity and CO classification are 89.70% and 83.43%. Comparison of 1D CNN, decision tree, and linear regression model revealed that the machine learning models considerably outperform linear regression model, and they can better capture the nonlinear correlations among the variables. Furthermore, the predictive outcomes of the models were consistent across different weather conditions. The proposed methodology can be used to induce tolerance against faulty reads at sensor level in greenhouse sensor networks, independent of time and the data gathered by the faulty sensors. The study indicates that exploiting cloud resources for promoting the use of complex AI models on IoT platforms can provide a suitable solution for real-time monitoring and fault control of greenhouse environmental factors.

Indirect non-linear effects of landscape patterns on vegetation growth in Kunming City

Urban greening is becoming an important strategy in improving urban ecosystem services and sustainability. Identifying the response of vegetation to urbanization and urban landscape patterns can help in planning for urban greening. Urbanization may lead to both direct and indirect effects on vegetation, and the indirect effects of urbanization on vegetation growth (UIE-VG) have been paid much attention recently in large scale. In this study, we investigated the spatiotemporal evolution of UIE-VG and the effects of landscape patterns on UIE-VG using the boosted regression tree model and remotely sensed data. An increase in average UIE-VG from 4 to 56% was found during urbanization of Kunming, the case study area in southwest China. However, UIE-VG exhibited high variations due to landscape pattern changes at the local scale. Overall, area-related and aggregation-related landscape metrics had greater effects on UIE-VG than the other metrics. The increase and aggregation of built-up land enhanced UIE-VG by 3.1–81.3% while the increase and aggregation of unused land and waterbodies reduced UIE-VG by 0.7–20.6%. Moreover, we found that the large and aggregated vegetation areas may mitigate the negative UIE-VG in low urbanization areas. Our findings have important implications for integrating urban landscape planning into sustainable urban greening strategies.

Applying machine learning to characterize and extrapolate the relationship between seismic structure and surface heat flow

SUMMARY Geothermal heat flow beneath the Greenland and Antarctic ice sheets is an important boundary condition for ice sheet dynamics, but is rarely measured directly and therefore is inferred indirectly from proxies (e.g. seismic structure, magnetic Curie depth, surface topography). We seek to improve the understanding of the relationship between heat flow and one such proxy—seismic structure—and determine how well heat flow data can be predicted from the structure (the characterization problem). We also seek to quantify the extent to which this relationship can be extrapolated from one continent to another (the transportability problem). To address these problems, we use direct heat flow observations and new seismic structural information in the contiguous United States and Europe, and construct three Machine Learning models of the relationship with different levels of complexity (Linear Regression, Decision Tree and Random Forest). We compare these models in terms of their interpretability, the predicted heat flow accuracy within a continent and the accuracy of the extrapolation between Europe and the United States. The Random Forest and Decision Tree models are the most accurate within a continent, while the Linear Regression and Decision Tree models are the most accurate upon extrapolation between continents. The Decision Tree model uniquely illuminates the regional variations of the relationship between heat flow and seismic structure. From the Decision Tree model, uppermost mantle shear wave speed, crustal shear wave speed and Moho depth together explain more than half of the observed heat flow variations in both the United States [$r^2 \approx 0.6$ (coefficient of determination), $\mathrm{RMSE} \approx 8\, {\rm mW}\,{\rm m}^{-2}$ (Root Mean Squared Error)] and Europe ($r^2 \approx 0.5, \mathrm{RMSE} \approx 13\, {\rm mW}\,{\rm m}^{-2}$), such that uppermost mantle shear wave speed is the most important. Extrapolating the U.S.-trained models to Europe reasonably predicts the geographical distribution of heat flow [$\rho = 0.48$ (correlation coefficient)], but not the absolute amplitude of the variations ($r^2 = 0.17$), similarly from Europe to the United States ($\rho = 0.66, r^2 = 0.24$). The deterioration of accuracy upon extrapolation is caused by differences between the continents in how seismic structure is imaged, the heat flow data and intrinsic crustal radiogenic heat production. Our methods have the potential to improve the reliability and resolution of heat flow inferences across Antarctica and the validation and cross-validation procedures we present can be applied to heat flow proxies other than seismic structure, which may help resolve inconsistencies between existing subglacial heat flow values inferred using different proxies.

Nonpharmacological pain management approaches among U.S. construction workers: A cross-sectional pilot study.

U.S. construction workers experience high rates of injury that can lead to chronic pain. This pilot study examined nonpharmacological (without medication prescribed by healthcare provider) and pharmacological (e.g., prescription opioids) pain management approaches used by construction workers. A convenience sample of U.S. construction workers was surveyed, in partnership with the U.S. National Institute for Occupational Safety and Health (NIOSH) Construction Sector Program. Differences in familiarity and use of nonpharmacological and pharmacological pain management approaches, by demographics, were assessed using logistic regression models. A boosted regression tree model examined the most influential factors related to pharmacological pain management use, and potential reductions in use were counterfactually modeled. Of 166 (85%) of 195 participants reporting pain/discomfort in the last year, 72% reported using pharmacological pain management approaches, including 19% using opioids. There were significant differences in familiarity with nonpharmacological approaches by gender, education, work experience, and job title. Among 37 factors that predicted using pharmacological and non-pharmacological pain management approaches, training on the risks of opioids, job benefits for unpaid leave and paid disability, and familiarity with music therapy, meditation or mindful breathing, and body scans were among the most important predictors of potentially reducing use of pharmacological approaches. Providing these nonpharmacological approaches to workers could result in an estimated 23% (95% CI: 16%-30%) reduction in pharmacological pain management approaches. This pilot study suggests specific factors related to training, job benefits, and worker familiarity with nonpharmacological pain management approaches influence use of these approaches.

Soil-based assessment of site productivity for southern pine plantations in the coastal plain of the southeastern US: (II) slash pine

Non-industrial private forest landowners and foresters often rely on outdated site index models for forest productivity predictions, which hinders accurate assessment. To address this issue, 157 slash pine plots were established in the coastal plain of Georgia, USA, to collect tree and soil data. Using regression tree and linear regression models, we identified modified Cooperative Research in Forest Fertilization soil groups and surface soil extractable phosphorus as significant predictors of site index. The optimal regression tree model (R2 = 0.490, RMSE = 1.72 m) outperformed the linear regression model. Updated site index and simulated mean annual increments for slash pine were compared with long-term experimental study data, as well as with previous findings for loblolly pine in the same region (Zhao et al., 2024a). The new predictive models will help non-industrial private forest landowners evaluate slash pine productivity in the Coastal Plain region, applicable across diverse sites.

Machine learning to mechanically assess 2D and 3D biomimetic electrospun scaffolds for tissue engineering applications: Between the predictability and the interpretability

Currently, the use of autografts is the gold standard for the replacement of many damaged biological tissues. However, this practice presents disadvantages that can be mitigated through tissue-engineered implants. The aim of this study is to explore how machine learning can mechanically evaluate 2D and 3D polyvinyl alcohol (PVA) electrospun scaffolds (one twisted filament, 3 twisted filament and 3 twisted/braided filament scaffolds) for their use in different tissue engineering applications. Crosslinked and non-crosslinked scaffolds were fabricated and mechanically characterised, in dry/wet conditions and under longitudinal/transverse loading, using tensile testing. 28 machine learning models (ML) were used to predict the mechanical properties of the scaffolds. 4 exogenous variables (structure, environmental condition, crosslinking and direction of the load) were used to predict 2 endogenous variables (Young’s modulus and ultimate tensile strength). ML models were able to identify 6 structures and testing conditions with comparable Young’s modulus and ultimate tensile strength to ligamentous tissue, skin tissue, oral and nasal tissue, and renal tissue. This novel study proved that Classification and Regression Trees (CART) models were an innovative and easy to interpret tool to identify biomimetic electrospun structures; however, Cubist and Support Vector Machine (SVM) models were the most accurate, with R2 of 0.93 and 0.8, to predict the ultimate tensile strength and Young’s modulus, respectively. This approach can be implemented to optimise the manufacturing process in different applications.

Neighborhood externalities modify the nexus between residential land and housing prices: A joint analysis using regression tree models

Neighborhood externalities influence residential land prices and housing prices simultaneously, contributing to a nexus relationship between them. Most existing studies have focused on the causal relationship between the two, with limited attention to their nexus. This research matched land parcel transactions from 2003 to 2015 and housing transaction records with purchasers’ housing purchase records before 2019 to construct a spatial-temporal land-housing dataset in Shanghai. Both spatial statistical tools and regression tree models were employed to explore the underlying mechanism, focusing on proximity to industries. According to the modeling results, residential land prices and housing prices are highly sensitive to polluting industries and producer service industries. The impacts of polluting industries on residential land prices have intensified, but the impacts on housing prices have not. The regression tree models further showed that living service industries were the primary positive contributor to residential land and housing prices. Regarding the social environment, non-local people are taking over communities with high-priced housing on high-value residential land parcels. These research outcomes encourage more efforts to explore the determinants of the nexus between residential land prices and housing prices, rather than discussion of the causality relationship.

A supervised learning tool for heatwave predictions using daily high summer temperatures

AbstractGlobal temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions.

Age-related behavioral resilience in smartphone touchscreen interaction dynamics

We experience a life that is full of ups and downs. The ability to bounce back after adverse life events such as the loss of a loved one or serious illness declines with age, and such isolated events can even trigger accelerated aging. How humans respond to common day-to-day perturbations is less clear. Here, we infer the aging status from smartphone behavior by using a decision tree regression model trained to accurately estimate the chronological age based on the dynamics of touchscreen interactions. Individuals (N = 280, 21 to 87 y of age) expressed smartphone behavior that appeared younger on certain days and older on other days through the observation period that lasted up to ~4 y. We captured the essence of these fluctuations by leveraging the mathematical concept of critical transitions and tipping points in complex systems. In most individuals, we find one or more alternative stable aging states separated by tipping points. The older the individual, the lower the resilience to forces that push the behavior across the tipping point into an older state. Traditional accounts of aging based on sparse longitudinal data spanning decades suggest a gradual behavioral decline with age. Taken together with our current results, we propose that the gradual age-related changes are interleaved with more complex dynamics at shorter timescales where the same individual may navigate distinct behavioral aging states from one day to the next. Real-world behavioral data modeled as a complex system can transform how we view and study aging.

Seismic Features Predict Ground Motions During Repeating Caldera Collapse Sequence

AbstractApplying machine learning to continuous acoustic emissions, signals previously deemed noise, from laboratory faults and slowly slipping subduction‐zone faults, demonstrates hidden signatures are emitted that describe physical details, including fault displacement and friction. However, no evidence currently exists to demonstrate that similar hidden signals occur during seismogenic stick‐slip on earthquake faults—the damaging earthquakes of most societal interest. We show that continuous seismic emissions emitted during the 2018 multi‐month caldera collapse sequence at the Kı̄lauea volcano in Hawai'i contain hidden signatures characterizing the earthquake cycle. Multi‐spectral data features extracted from 30 s intervals of the continuous seismic emission are used to train a gradient boosted tree regression model to predict the GNSS‐derived contemporaneous surface displacement and time‐to‐failure of the upcoming collapse event. This striking result suggests that at least some faults emit such signals and provide a potential path to characterizing the instantaneous and future behavior of earthquake faults.

Soil-based assessment of site productivity for southern pine plantations in the coastal plain of the southeastern US: (I) loblolly pine

Site productivity and forest management strategies hinge on intrinsic site resources such as soil water and nutrient availability. In this study, regression tree model and linear regression model approaches were employed to quantify the relationship between site index and soil parameters for loblolly pine plantations in the Coastal Plain. Both the optimal regression tree model and the final linear regression model identified modified CRIFF (Cooperative Research in Forest Fertilization) soil groups and surface soil extractable phosphorus (Mehlich 1 P) as significant variables for predicting site index. The final linear regression model, with a R2 of 0.899 and an RMSE of 1.12 m, exhibited superior performance compared to the optimal regression tree model. Additionally, mean annual increments in stand volume and green weight for each soil group were simulated and compared with data from a long-term loblolly pine culture/density experimental study in the same region. These resulting predictive models offer applicability across diverse sites and are particularly well-suited for areas where forest stands are not currently established.

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D-printed Polylactic Acid (PLA) specimens. We studied the effects of five process parameters, including the build orientation, infill angle, layer thickness, printing speed, and nozzle temperature, on the printed parts tensile strength and surface roughness. Machine learning models are developed using the experimental data collected from the printed 27 specimens. Gradient Boosting Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression, Random Forest Regression, and Extremely Randomized Tree Regression models were developed during the machine learning modeling stage to predict the surface roughness and tensile strength of the printed parts. This research demonstrates the effectiveness of Extremely Randomized Tree Regression model in providing accurate tensile strength predictions with root mean square error (RMSE) of 1.03, mean absolute error (MAE) of 0.82, and mean absolute percentage error (MAPE) of 2.20%. Similarly, Random Forest Regression model shows better accuracy in predicting surface roughness having RMSE of 0.408, MAE of 0.31, and MAPE of 9.28%. Moreover, the comparative study confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor machine learning models for predicting the surface roughness and tensile strength of the printed parts. The results highlight a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset, establishing the foundation for improved product design and mechanical property optimization through adjustment of the process parameters combination.

The influence of scaffolding on intrinsic motivation and autonomous adherence to a game-based, unsupervised home rehabilitation program for people with upper extremity hemiparesis due to stroke. A randomized controlled trial.

This parallel, randomized controlled trial examines intrinsic motivation, adherence and motor function improvement demonstrated by two groups of subjects that performed a twelve-week, home-based upper extremity rehabilitation program. Seventeen subjects played games presenting eight to twelve discrete levels of increasing difficulty. Sixteen subjects performed the same activities controlled by success algorithms that modify game difficulty incrementally. 33 persons 20 to 80 years of age, at least six months post stroke with moderate to mild hemiparesis were randomized using a random number generator into the two groups. They were tested using the Action Research Arm Test, Upper Extremity Fugl Meyer Assessment, Stroke Impact Scale and Intrinsic Motivation Inventory pre and post training. Adherence was measured using timestamps generated by the system. Subjects had the Home Virtual Rehabilitation System [1]systems placed in their homes and were taught to perform rehabilitation games using it. Subjects were instructed to train twenty minutes per day but were allowed to train as much as they chose. Subjects trained for twelve weeks without appointments and received intermittent support from study staff. Group outcomes were compared using ANOVA. Correlations between subject demographics and adherence, as well as motor outcome, were evaluated using Pearson Correlation Coefficients. Classification and Regression Tree (CART) models were generated to predict responders using demographics and baseline measures. There were 5 dropouts and no adverse events. The main effect of time was statistically significant for four of the five clinical outcome measures. There were no significant training group by time interactions. Measures of adherence did not differ between groups. 21 subjects from both groups, demonstrated clinically important improvements in UEFMA score of at least 4.25 points. Subjects with pre training UEFMA scores below 53.5 averaged a seven-point UEFMA increase. IMI scores were stable pre to post training. Scaffolding did not have a meaningful impact on adherence or motor function improvement. A sparsely supervised program of game-based treatment in the home was sufficient to elicit meaningful improvements in motor function and activities of daily living. Common factors considered barriers to the utilization of telerehabilitation did not impact adherence or motor outcome. Clinical Trials.gov - NCT03985761, Registered June 14, 2019.

NAVIGATING ASSESSMENT ALIGNMENT IN BLENDED LEARNING: A COMPARATIVE ANALYSIS OF MAJOR AND NON-MAJOR SUBJECTS

A powerful pedagogical strategy has evolved in the age of developing educational technology: blended learning, which combines traditional instruction with online resources. In particular, it examines distinctions between major and non-major subjects in order to address the essential issue of assessment alignment in blend-ed learning. While investigations frequently evaluate the effectiveness of blended learning, few examine how examinations fit with its distinctive features. The assessment alignment in major and non-major subjects is compared in this study using the Correlation and Regression Tree (CART) model. Determining how assessments should align and how to make sure they measure topic mastery in this blended setting are among the key issues discussed. The study contributes to conversations about curriculum design and policy by assessing the literature and examining assessment procedures. The study offers knowledge that can be used to create evaluations for blended learning that are efficient while also promoting engagement among learners and academic success. Through this investigation, teachers receive tips on instructional design that will help them in the digital age to promote a unified learning experience across all topics.

Prediction Model for Delayed Behavior of Early Ambulation After Surgery for Varicose Veins of the Lower Extremity: A Prospective Case-Control Study

ObjectiveTo analyze influencing factors and establish a prediction model for delayed behavior of early ambulation after surgery for varicose veins of the lower extremity (VVLE). DesignA prospective case-control study. SettingPatients with VVLE were recruited from 2 local hospitals. ParticipantsIn total, 498 patients with VVLE were selected using convenience sampling and divided into a training set and a test set. InterventionsNot applicable. Main Outcome MeasuresWe collected information from the selected participants before surgery and followed up until the day after surgery, then divided them into a normal and delayed ambulation group. Propensity score matching was applied to all participants by type of surgery and anesthesia. All the characteristics in the 2 groups were compared using logistic regression, back propagation neural network (BPNN), and decision tree models. The accuracy, sensitivity, specificity, and area under the curve (AUC) values of the 3 models were compared to determine the optimal model. ResultsA total of 406 participants were included after propensity score matching. The AUC values for the training sets of logistic regression, BPNN, and decision tree models were 0.850, 0.932, and 0.757, respectively. The AUC values for the test sets were 0.928, 0.984, and 0.776, respectively. A BPNN was the optimal model. Social Support Rating Scale score, preoperative 30-second sit-stand test score, Clinical-Etiology-Anatomy-Pathophysiology (CEAP) grade, Medical Coping Modes Questionnaire score, and whether you know the need for early ambulation, in descending order of the result of a BPNN model. A probability value greater than 0.56 indicated delayed behavior of early ambulation. ConclusionsClinicians should pay more attention to those with lower Social Support Rating Scale scores, poor lower limb strength, a higher CEAP grade, and poor medical coping ability, and make patients aware of the necessity and importance of early ambulation, thereby assisting decision-making regarding postoperative rehabilitation. Further research is needed to improve the method, add more variables, and transform the model into a scale to screen and intervene in the delayed behavior of early ambulation of VVLE in advance.

AI-POWERED FRAUD DETECTION IN BANKING: SAFEGUARDING FINANCIAL TRANSACTIONS

The banking industry's metamorphosis through digitalization has unquestionably revolutionized accessibility and convenience for customers worldwide. However, this paradigm shift has ushered in a new era of challenges, most notably in the realm of cybersecurity. Conventional rule-based fraud detection strategies have struggled to keep pace with the rapid evolution of cyber threats, prompting a surge of interest in more adaptive approaches like unsupervised learning. Furthermore, the COVID-19 pandemic has exacerbated the issue of bank fraud due to the widespread transition to online platforms and the proliferation of charitable funds, which present ripe opportunities for exploitation by cybercriminals. In response to these pressing concerns, this study delves into the realm of machine learning algorithms for the analysis and identification of fraudulent banking transactions. Notably, it contributes scientific novelty by developing models specifically tailored to this purpose and implementing innovative preprocessing techniques to enhance detection accuracy. Utilizing a diverse array of algorithms, including Random Forest, K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees, and Logistic Regression, the study showcases promising results. In particular, logistic regression and decision tree models exhibit impressive accuracy and Area Under the Curve (AUC) values of approximately 0.98, 0.97 and 0.95, 0.94, respectively. Given the pervasive nature of banking fraud in our digital society, the utilization of artificial intelligence algorithms for fraud detection stands as a critical and timely endeavor, promising enhanced security and trust in the financial ecosystem.

A Statiscal Analysis of the Role Various of Co-variables in Detemining, Number of Nodes to be Dissected in Endometrial Cancer by Cart Model, Ahpgic

Aim and Objective: The main objective of this paper is to predict the role of covariables in determining the number of nodes to be dissected in endometrial cancer, using the best regression model. Also, to compare the accuracy of the cart model with the traditional regression model, to accurately find and predict the co-variable in determining the number of nodes to be dissected. Material Method: Data on 170 endometrial cancer patients with their covariates were collected from the institute ahpgic and used for the said objectives. The data for this paper was collected wherein dependent variable is total number of lymph node involved and 10 co-variates (independent variables) age, Postmenopausal Bleeding, Obstetrics History, Nodal Status, Tumor Size, Histology, Grade, Myometrial Invasion, Lymphovascular Space Invasion and Cervical Extension. Methods: multiple regression and cart model. Results: Average number of lymph node dissection among patients having tumor size less than 1.9 cm is 3.73 (approx4) and the patients having a tumor size 1.9 cm is 12.4 (approx13). Average nos of nodes dissection among the patients having prior dissected nodal staus as b/l pelvic lymphdenectomy 10.9 (approx 11) and patients having prior dissected b/l paraortic + b/l pelvic lymhadenectomy is 14.1 (approx14). Thus CART MODEL can predict with more accuracy of 95.9% than the multiple regression model which is of 88.3%, based on the selected covariates and validated by receiver operating characteristics (ROC) curve. Conclusion: Thus we conclude that if tumor size >1.9cm (approx> 2cm) the 12 nodes and if less than 1.9cm (approx< 2cm) then 3.73 i.e approx 4 nodes should be dissected We found that classification and regression tree (CART) model is able to predict the role of the co variate i.e tumor size in deciding the number of lymph node dissection for the EC patients with an accuracy of 95.9% based on the selected covariates and validated by receiver operating characteristics (ROC) curve. Thus CART MODEL can predict with more accuracy of 95.9% than the multiple regression model which is of 88.3%, based on the selected covariates and validated by receiver operating characteristics (ROC) curve. Purpose: The above study will be of help in deciding the nos of nodes to be dissected in endometrial cancer so, that unnecessary morbidities like bleeding and lymphoedem, and waste of resources i.e money and time.

Evaluation of Rural Healing Landscape DESIGN Based on Virtual Reality and Electroencephalography

From the user’s perspective, emotional elements are increasingly being used in design. Researchers have indicated that healing landscapes in rural areas play a positive role in soothing human emotions. In this study, a landscape with healing functions was designed, and 32 subjects experienced emotions in a virtual reality (VR) scene while their 32-channel electroencephalography (EEG) signals were collected. This study compared the brain responses with and without the presence of healing landscape elements and conducted correlation coefficient analysis using eight different regression prediction models to examine the relationship between security, comfort, positivity, and corresponding healing landscape elements. The results show significant improvements in emotions of security, comfort, and positivity post-exposure to the landscape design, especially with certain elements, such as seating, shrubs, and tree pools. EEG data indicate enhanced emotional and cognitive states, particularly relaxation, with increased activity in specific brain regions. The decision tree regression model is the most suitable for our data. It reveals strong correlations between specific healing landscape elements and emotional responses. In the comfort category, “shrubs” show the highest correlation (R² = 0.82), while in the security category, “trees” have the highest correlation (R² = 0.77). Similarly, in the positivity category, “trees” again exhibit the highest correlation (R² = 0.71) with EEG data, indicating their significant impact on these emotional dimensions. This study demonstrates the importance of using scientific methods, such as EEG technology, to validate the principles of emotional design and also underscores the role of green environments in enhancing psychological health and emotional comfort.

Analysis and Implementation of University Teacher Salary Prediction Based on Decision Tree Regression Model

This study is based on the decision tree regression model, utilizing the Python programming language and third-party libraries such as Scikit-learn, to mine and analyze data of university teachers over the past five years. A decision tree regression model is constructed, enabling the prediction and analysis of university teacher salaries. This achievement provides university administrators with a more scientific, objective, and efficient decision-making reference, aiding in the construction of a more scientific and systematic budget management system for universities.

Type I Tobit Bayesian Additive Regression Trees for censored outcome regression

Censoring occurs when an outcome is unobserved beyond some threshold value. Methods that do not account for censoring produce biased predictions of the unobserved outcome. This paper introduces Type I Tobit Bayesian Additive Regression Tree (TOBART-1) models for censored outcomes. Simulation results and real data applications demonstrate that TOBART-1 produces accurate predictions of censored outcomes. TOBART-1 provides posterior intervals for the conditional expectation and other quantities of interest. The error term distribution can have a large impact on the expectation of the censored outcome. Therefore, the error is flexibly modeled as a Dirichlet process mixture of normal distributions. An R package is available at https://github.com/EoghanONeill/TobitBART.