Regression Equation Research Articles

The development and evaluation of multiple regression equations based on four common nutritional analysis packages to predict the metabolisable energy density of diets fed to grower/finisher and adult pigs and their use for rat and mouse diets.

We have used multiple regression analyses to develop a series of metabolisable energy (ME) prediction equations from chemical analyses of pig diets that can be extended to murine diets. We compiled four datasets from an extensive range of published metabolism studies with grower/finisher and adult pigs. The analytes in the datasets were increasingly complex, comprising: 1. the proximate or Weende analysis, 2. the previous analysis but with neutral detergent fibre (NDF) replacing crude fibre, 3. the NDF package plus starch, 4. the NDF package plus starch and sugars. Diet manufacturers routinely provide most of the analytes for batches of murine diet, or they are easily obtainable. The study uniquely compares the four analytical packages side-by-side. The number of records in the datasets varies from 367 to 827. With increasing analytical complexity, adjusted R2 values for ME prediction improved from 0.751 to 0.869, and the mean absolute error from 0.422 to 0.289 kJ/g. Overall, the models' prediction interval (PI) improved from 1 to 0.7 kJ/g, which is ± 7% to 5% for a typical dietary ME density of 14.8 kJ/g. Although prediction accuracy increases as one extends the range and complexity of the analytes measured, the improvement is slight and may not justify the substantial increase in analytical cost. The equations were validated for use on future data sets by k-fold analysis. Although the equations are developed from pig data, they are suitable for rat and mouse diets, based on comparable digestibility measurements, and substantially improve existing methods.

Pengaruh Minat dan Kemandirian Belajar Terhadap Hasil Belajar Matematika Siswa di Kelas VII MTsS TI

The low student mathematics learning outcomes are caused by several problems, namely the low interest and independence of student learning which is the driving force for conducting this research. Ex Post Facto research using quantitative techniques is this type of research. The research population was 118 class VIII MTsS TI Candung students. The instrument used was a questionnaire on interest and independence in learning. The research results produce a regression equation Y= 11.683 + 0.498 representing interest in learning on learning outcomes. t count = 3.113 > t table = 2.052 is the result of the regression significance test. Interest in learning has a fairly large partial influence on mathematics learning outcomes. Y= 16.171 + 0.525 is the regression equation for learning independence on learning outcomes. After carrying out the regression significance test, it was obtained that t = 2.472 > t table = 2.052. Partially, learning independence has a significant effect on mathematics learning outcomes. The resulting multiple regression equation has a determinant coefficient KD = 38.247% and a correlation coefficient value of 0.618 indicating a moderate relationship. The multiple regression significance test produces calculated F = 13.031 > F table = 3.37. It is known that interest and independence have a big influence on mathematics learning outcomes.

Artificial intelligence in financial auditing: improving efficiency and addressing ethical and regulatory challenges

Artificial Intelligence has been increasingly reshaping the face of financial auditing for improved efficiency and effectiveness in fraud detection, serving also to strengthen stakeholder trust. This study examines the impact of adopting AI on audit practices, in terms of effectiveness in improving efficiency, fraud detection, ethical challenges, regulatory barriers, and stakeholder trust. In this paper, 460 professional auditors, accountants, and organizational stakeholders have been analyzed using descriptive statistics, correlation, regression, and structural equation modeling. The results indicate that AI has a positive influence on efficiency, value creation, and fraud detection ability, while positively influencing stakeholder trust in organizations. However, ethical concerns from algorithmic bias to a lack of transparency and regulatory risks associated with compliance through data protection laws are also significant barriers. It thus concludes that AI has a enormous potential in revolutionizing auditing practices, and that addressing these barriers through training, transparent AI models, ethical safeguards, and supportive regulatory frameworks is also very important for its widespread adoption. Recommendations and future research avenues are presented to guide the responsible integration of AI into the auditing profession.

Internet use and attitude toward aging among Chinese older adults: the mediating role of health

ObjectivesThis research seeks to explore the relationship between Internet use and attitudes toward aging among older adults in China, with a particular emphasis on the mediating role of health in this correlation.MethodsA national survey of 10,858 Chinese adults aged 60 and above was conducted, employing multiple linear regression and structural equation modeling to analyze the impact of Internet use on aging attitudes with health as a mediator.ResultsThe research found a significant positive association between Internet use and positive aging attitudes (β = −1.420, p < 0.001). Health indicators—self-rated, physical, and mental health—were identified as key moderators, with healthier adults benefiting more from Internet use.ConclusionThis research reveals a positive association between Internet use and attitudes toward aging among older adults in China. These findings hold substantial implications for encouraging active and healthy aging in the digital era, underscoring the value of advocating for digital engagement among seniors as a means to improve their quality of life.

Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR

Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management. Commercial software primarily relies on manual classification, which is time-intensive. This study addresses these challenges by using the laser reflectance intensity (I-value) for automated classification. Boxplot theory is applied to calibrate the data, remove noise, and establish polynomial regression equations correlating intensity with scanning distances. These equations serve as attribute functions for classifying datasets. Focusing on materials in traditional Minnan architecture on Kinmen Island, controlled indoor experiments and outdoor case studies validate the approach. The results show classification accuracies of 74% for wood, 98% for stone, and 93% for brick, demonstrating this method’s effectiveness in enhancing point cloud data applications and management.

EVALUASI EFEKTIVITAS SERVANT LEADERSHIP DAN MOTIVASI KERJA TERHADAP KINERJA PERSONIL SATPAS DALAM MENINGKATKAN PELAYANAN PENERBITAN SIM DI WILAYAH HUKUM POLRESTABES PALEMBANG

This research is motivated by the quality of the National Police's Human Resources (HR) and public services which are not yet optimal, Standard Operating Procedures (SOP) which are still complicated, vocational school-based assessments which are still not running well, the integrity of officers is weak, and leadership style is something that must continue to be improved in the SIM service. The purpose of this research is to determine the evaluation of the effectiveness of servant Leadership and work motivation on the performance of Satpas personnel in improving SIM issuance services in the Palembang Police Legal Area. The concept or theory of Servant Leadership (X1) from Yulk (2016:62), work motivation (X2) is explained by Uno's theory (2017:53) and performance (Y) uses Robbins' theory (2015:76). This research uses quantitative methods with a cross-sectional research design. The target population in this research is the Palembang Police Traffic Unit personnel, 176 people who work at the Palembang Police. Meanwhile, the target population is 36 Satpas personnel. The data collection technique uses a 50-item questionnaire with a Likert scale. The data analysis technique used is Multiple Linear Regression Analysis. The research results show that the calculated F value is 425.013 with a significance level of 0.000. The t value is 7.538 with a significance value between Servant Leadership and the performance of Satpas personnel in improving SIM issuance services of 0.000. The multiple linear regression equation is ????̂ = 0.453 + 0.615X1 + 0.241

Optimization of Mold Heating Structure Parameters Based on Cavity Surface Temperature Uniformity and Thermal Response Rates

Rapid heating cycle molding technology has recently emerged as a novel injection molding technique, with the uniformity of temperature distribution on the mold cavity surface being a critical factor influencing product quality. A numerical simulation method is employed to investigate the rapid heating process of molds and optimize heating power, with the positions of heating rods as variables. The temperature uniformity coefficient is an indicator used to assess the uniformity of temperature distribution within a system or process, while the thermal response rate plays a crucial role in evaluating the heating efficiency of a heating system. The thermal response rate of the cavity and the temperature uniformity coefficient are set as optimization objectives to define parameter ranges for orthogonal experiments. The findings indicate that the optimal range for the lateral distance L1 is 20–30 mm, for L2 it is 50–70 mm, and for the vertical distance (h) it is 3–8 mm. The response surface multiple regression equation derived from the orthogonal experiment data demonstrates a model prediction error rate of 1.8% and 2.4%. Additionally, by applying particle swarm optimization to the regression equation, the study identifies an optimal scheme that reduces system energy consumption by 12.5%, achieves a thermal response rate of 0.75 k/s, decreases the temperature uniformity coefficient by 44.6%, and lowers the temperature difference by 52.17%. This optimization ensures efficient heating of the mold cavity, reduces energy consumption, and enhances the uniformity of the surface temperature distribution, ultimately improving the surface quality of the products.

Jump-Preserving Estimation and Jump Detection for Nonparametric Model with Missing Covariate

Nonparametric regression analysis has broad applications. In some cases, the regression function with jumps (i.e., the regression curve is discontinuous) seems to be more appropriate to describe the related phenomena. Existing a number of methods for estimating discontinuous curve, most of which are based on the data is complete, which is unrealistic in many practical situations. In this paper, we consider estimating discontinuous nonparametric model with missing covariate data. Based on inverse selection probability weighted and jump-preserving techniques, a jump-preserving estimation procedure is proposed. The proposed method is capable of automatically accommodating possible jumps in the nonparametric function, without the requirement of prior knowledge regarding the number and locations of jump points. The proposed estimator for the discontinuous regression function is shown to be oracally efficient in the sense that it is uniformly indistinguishable from that when the selection probabilities are known. Furthermore, it is proved that the fitted curve by this procedure is consistent in the entire design space. Numerical simulation also indicates the performance of finite sample of this method is efficient and reliable.

Optimization of Material Removal Rate in EDM Machining of OHNS Steel

The experimental investigation of material removal rate during machining of OHNS steel using EDM machine was study in this paper. The input parameters include peak current, pulse on time, Pulse off time were used for experimental work S/N ratio graphs have been used to optimize the machining parameters of EDM on OHNS steel using the Taguchi method and ANOVA methods. The study will carry out to investigate the effect of which parameter has the largest effect on the material removal rate of OHNS steel by machining. The optimum machining condition for material removal rate (MRR) has been find out with the ANOVA analysis and regression equation. This study is carried out by using the experimental investigation method and Taguchi analysis, at the end of this study we come to the conclusion of optimum conditions of the machining for OHNS material. Key Words: S/N, ANOVA, MRR, Taguchi, EDM, OHNS.

Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake.

Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.

Household Exposure to Substance Abuse and the Risk of New Persistent Opioid Use After Cancer Surgery.

Household-level factors may also influence the risk of new persistent opioid use (NPOU). We sought to assess the risk of NPOU among individuals with household exposure to substance abuse. Opioid-naïve individuals who underwent oncological procedures were identified using the IBM MarketScan database. Multivariable regression equations were used to assess the association between household exposure to substance abuse and NPOU. Among 10 557 individuals 4.9% (n = 520) had a family history of substance abuse. Patients with a family history of substance abuse were more likely to use opioids during the perioperative (85.0% vs. 78.8%) and postoperative periods (40.2% vs. 34.1%) (both p < 0.05). Moreover, perioperative (300 MME [IQR: 450-200] vs. 250 MME [IQR: 150-435]) and postoperative (525 MME [IQR: 1348-225] vs. 350 MME [IQR: 150-850]) opioid dose was also higher among patients with a family history of substance abuse (both p < 0.001). On multivariate analysis, patients with family history of substance abuse had 37% (OR 1.37, 95% CI 1.07-1.75) higher odds of NPOU. Among opioid-naïve individuals undergoing oncological procedures, a family history of substance abuse is associated with NPOU. Patients should be screened for household-level factors before prescribing opioids for perioperative use.

Effect of Amino Acids on the Synthesis of NiFe2O4/Au Hybrid Nanoparticles

Hybrid nanoparticles, composed of magnetic oxides and gold, have garnered significant interest due to their potential applications in various fields, including catalysis, diagnostics, and nanomedicine. In this study, the effect of reaction parameters on the reduction of HAuCl4 by different non-sulfur amino acids (glycine, L-serine, L-tryptophan, and L-tyrosine) was determined using the design of experiment (DOE) method. The results of the analysis of the regression equations were used to select the conditions and develop a methodology for the preparation of the hybrid magnetic NiFe2O4/Au nanoparticles (NPs) by direct reduction of gold on the magnetic surface using the aforementioned amino acids as the reducing and stabilizing agents simultaneously. The materials were characterized using XRD, TEM, XPS, and Vis/FTIR spectroscopy. The results indicate the successful synthesis of magnetic NiFe2O4/Au nanoparticles with all amino acids used, but the size of the gold crystals, their surface density, and the details of the NP structure (inlaid or a core–shell structure) depend on the amino acid used. The mechanism of the gold deposition on the magnetic core surface and the difference in the effect of various amino acids are discussed. The developed synthesis strategy can be extended to other metal ferrites and iron oxides.

Comparing Above and Underwater Visibility of Fluorescent Quinine-Based Liquid and Solid Tracers to Estimate Suspended Sediment Concentrations Under Low Luminosity Conditions

While exploring the use and visibility of fluorescent tracers for estimating flow velocities under varying water turbidity, this study introduces a technique for estimating suspended sediment concentration (SSC) in water. Laboratory and field experiments were conducted using a dual above/underwater optical camera monitoring system to measure and compare the visibility (brightness intensity) of fluorescent tracers applied into water under varying SSC loads. Based on the fluorescent properties of quinine under low luminosity conditions, a quinine solution, in both liquid and solid (ice cube) states, was applied to the water surface. Its visibility was recorded using the monitoring system, and brightness intensity was quantified using image processing techniques such as grayscale conversion, object segmentation, and pixel intensity extraction. Results indicate that the tracer brightness intensity decreases as SSC loads increase, with the underwater camera recording lower visibility than the above-water camera, particularly at higher SSC loads. Empirical regression equations were developed to describe the relationship between the tracer brightness intensity recorded by the underwater camera and the SSC loads. These findings suggest the ability of the presented monitoring system, using quinine solution in both liquid and solid states, along with an underwater camera, to estimate a relevant range of SSC loads under low luminosity conditions.

Physicochemical Properties of Jet-A/n-Heptane/Alcohol Blends for Turboengine Applications

This work investigated the physical properties of Jet-A blended with n-heptane and various n-alcohols. The mixtures contained 10%, 20%, and 30% n-alcohols, including n-propanol, n-butanol, n-pentanol, n-hexanol, n-heptanol, and n-octanol. These alcohols are either derived from biomass or have significant potential for bio-based production. The blends were assessed against American Society for Testing and Materials (ASTM) D1655 standards for Jet-A in terms of the density, viscosity, and flash point. Additionally, the refractive index and Fourier Transform Infrared Spectroscopy (FTIR) analysis were employed to gain insights into the blend chemical composition. Density measurements for the blends fell within the ASTM specifications (0.7939 to 0.8075 g·cm−3). Viscosity measurements at −20 °C were not directly conducted due to technical limitations. However, extrapolating viscosity–temperature data suggests that the blends would meet the ASTM standard. Flash point measurements revealed that all mixtures exhibited values below the ASTM specification of 38 °C. Regression equations were developed to estimate the density, kinematic viscosity, and refractive index of the studied mixtures as a function of alcohol volume. Furthermore, a correlation study was conducted to estimate density and viscosity from refractive index measurements, given their simplicity, and minimal sample volume requirements. The R2 values for these correlations exceeded 0.99, indicating a strong relationship between the refractive index and the other properties.

Culturally responsive teaching in higher education: the effects of personality and personal meaning profiles of academics on culturally responsive teaching self-efficacy / La enseñanza culturalmente receptiva en la Educación Superior: los efectos de la de personalidad y de los perfiles de significado personal de los académicos en la autoeficacia en educación culturalmente receptiva

In this study, we aimed to examine the factors affecting the culturally responsive teaching competencies of academics and tested whether the personality traits and personal meaning profiles of the academics had significant effects on culturally responsive teaching self-efficacy. Data were obtained from 1,883 academics by using the Culturally Responsive Teaching Self-Efficacy Scale, Ten-Item Personality Inventory and Personal Meaning Profile Scale — Short Form. Correlation, regression and structural equation modelling analyses were run to analyse the data. We found that culturally responsive teaching self-efficacy is low. In terms of personal meaning profiles, ‘openness to experience’, ‘agreeableness’ personality traits and ‘self-transcendence’ and ‘fair treatment’ had a significant effect on culturally responsive teaching self-efficacy while ‘religion’ and ‘self-acceptance’ were found to have no effect. Based on the findings, we made various suggestions for academics to participate in training on culture-sensitive teaching pedagogy to be more sensitive to different cultures.

Multimodal deep-learning model using pre-treatment endoscopic images and clinical information to predict efficacy of neoadjuvant chemotherapy in esophageal squamous cell carcinoma.

Neoadjuvant chemotherapy is standard for advanced esophageal squamous cell carcinoma, though often ineffective. Therefore, predicting the response to chemotherapy before treatment is desirable. However, there is currently no established method for predicting response to neoadjuvant chemotherapy. This study aims to build a deep-learning model to predict the response of esophageal squamous cell carcinoma to preoperative chemotherapy by utilizing multimodal data integrating esophageal endoscopic images and clinical information. 170 patients with locally advanced esophageal squamous cell carcinoma were retrospectively studied, and endoscopic images and clinical information before neoadjuvant chemotherapy were collected. Endoscopic images alone and endoscopic images plus clinical information were each analyzed with a deep-learning model based on ResNet50. The clinical information alone was analyzed using logistic regression machine learning models, and the area under areceiver operating characteristic curve was calculated to compare the accuracy of each model. Gradient-weighted Class Activation Mapping was used on the endoscopic images to analyze the trend of the regions of interest in this model. The area under thecurve by clinical information alone, endoscopy alone, and both combined were 0.64, 0.55, and 0.77, respectively. The endoscopic image plus clinical information group was statistically more significant than the other models. This model focused more on the tumor when trained with clinical information. The deep-learning model developed suggests that gastrointestinal endoscopic imaging, in combination with other clinical information, has the potential to predict the efficacy of neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma before treatment.

Household water security is a mediator of household food security in a nationally representative sample of Mexico.

Explore the relationship between water insecurity and food security and their covariates in Mexican households. A cross-sectional study with nationally representative data from the National Health and Nutrition Survey-Continuous 2021 (in Spanish, ENSANUT-Continua 2021), collected data from 12,619 households. Water insecurity was measured using the Household Water Insecurity Experiences (HWISE) Scale in Spanish and adapted to the Mexican context. Food security was measured using the Latin American and Caribbean Food Security Scale (ELCSA). A generalized path model was used to produce two simultaneous logistical regression equations--of water insecurity (WI, HWISE ≥12) and moderate-to-severe food insecurity (FI)-to understand key covariates as well as the contribution of WI to FI. The head of the household, an adult of >18 years of age, consented to participate in the survey. Households experiencing WI were more likely to experience moderate to severe FI (OR=2.35; 95%CI: 2.02-2.72). The odds of WI were lower in households with medium (OR=0.74; 95%CI: 0.61-0.9) to high (OR=0.45; 95%CI: 0.37-0.55) asset scores. Water insecurity also depended on the region of Mexico. FI is more prevalent in Indigenous people (OR=1.29; 95%CI: 1.05-1.59) and rural households (OR=0.42; 95%CI: 1.16-1.73). Notably, wealth and household size did not contribute directly to FI but did so indirectly through the mediating factor of WI. Our study shows that there are structural factors that form part of the varied determinants of water insecurity, which in turn is closely linked to food insecurity.

The prediction model of academic achievement based on cardiorespiratory fitness and BMI status for ninth-grade students

The purpose of this study was to predict an academic achievement model based on cardiorespiratory fitness (CRF) and body mass index (BMI) in ninth-graders. The study sample included 6 530 adolescents from 341 public schools in Slovakia. Criterion-referenced competency tests measuring academic performance in mathematics and mother language (Slovak), CRF, and BMI were assessed in the academic year 2022–2023. The results from the Random Forest Regression (RFR) machine learning algorithm suggest that adolescents who meet the international CRF and BMI criterion-referenced standards have a higher probability of getting a higher academic achievement score than unfit students with overweight or obesity. The chances of achieving the highest level of academic performance rose by 165% in mathematics and by 484% in mother language for boys who were fit and of normal weight compared to unfit boys with obesity. Unfit boys with obesity and unfit overweight girls had significantly lower odds of having the highest level of academic achievement compared to fit and normal-weight adolescents in mathematics (OR = 0.38; 95% CI, 0.20–0.71; p = 0.003; OR = 0.32; 95% CI, 0.18–0.55; p < 0.001) and mother language, respectively (OR = 0.17; 95% CI, 0.09–0.34; p < 0.001; OR = 0.17; 95% CI, 0.08–0.38; p < 0.001). Our results suggest that CRF is a significant predictor, with fit and normal-weight boys showing higher odds of better academic performance, but the model’s modest predictive power suggests other factors also play a role.

Characteristics, risk factors and a risk prediction model of tocilizumab-induced hypofibrinogenemia: a retrospective real-world study of inpatients

ObjectiveThe occurrence of hypofibrinogenemia after tocilizumab treatment has attracted increasing attention, which may cause bleeding and even life-threatening. This study aims to explore the risk factors for tocilizumab-induced hypofibrinogenemia (T-HFIB) and construct a risk prediction model.MethodsA total of 221 inpatients that received tocilizumab from 2015 to 2023 were retrospectively collected and divided into T-HFIB group or control group. The risk factors for T-HFIB were obtained by logistic regression equation and used to establish the nomogram.ResultsT-HFIB was observed in 121 of 221 patients (54.75%). Multifactorial logistic regression analysis revealed that infection (OR = 2.002, 95%CI:1.018 ~ 3.935), COVID-19 (OR = 3.752, 95%CI:1.264 ~ 11.139), CAR-T therapy (OR = 4.409, 95%CI:2.017 ~ 0.894), and concomitant glucocorticoids (OR = 5.303, 95%CI:0.227 ~ 0.894) were identified as independent risk factors for T-HFIB, while high baseline fibrinogen level (OR = 0.813, 95%CI:0.670 ~ 0.988) and concomitant antirheumatic drugs (OR = 0.451, 95%CI:0.227 ~ 0.894) were identified as protective factors. A nomogram was established, and area under the curve (AUC) of prediction model was 0.772 (95%CI:0.709 ~ 0.836). Calibration curve showed a good prediction accuracy for the occurrence of T-HFIB.ConclusionThe infection, COVID-19, CAR-T therapy, and concomitant glucocorticoids were independent risk factors for T-HFIB, while high baseline fibrinogen and concomitant antirheumatic drugs were protective factors. This nomogram can help early identify the patients at potential high risk of developing T-HFIB.

Exploring the determinants to accept dementia screening among patients at high risk of dementia based on the theory of planned behavior: A cross-sectional study.

Dementia represents a major public health challenge. Despite numerous initiatives promoting screening for early cognitive impairment to help prevent or delay its onset, participation remains limited. Moreover, there is limited evidence regarding screening intentions and predictors among populations at high risk of dementia. This study used the theory of planned behavior to identify modifiable factors associated with screening participation. This cross-sectional survey was conducted from November 16, 2021, to April 2, 2022, and involved 439 individuals at high risk of dementia selected using convenience sampling from two major regional communities in Guangzhou, China. A validated custom questionnaire based on the theory of planned behavior was used for data collection, and logistic regression and structural equation modeling were applied to identify related predictors. Overall, 62 % of the participants expressed willingness to undergo dementia screening. Behavioral intention was primarily associated with Perceived behavioral control (PBC, path coefficient = 0.36), Subjective norm (path coefficient = 0.16), and Attitude (path coefficient = 0.13). Additionally, PBC was associated with a positive attitude toward dementia screening (path coefficient = 0.39). PBC, subjective norm, and attitudes are associated with dementia screening willingness among high-risk groups. Healthcare professionals and policymakers should focus on enhancing favorable attitudes toward screening, increasing familial encouragement, and reducing perceived barriers to developing effective prevention or intervention strategies.