Multiple Regression Techniques Research Articles

The utilization of renal dialysis: a comprehensive study in Saudi Arabia

BackgroundUnderstanding the trend of utilization of renal dialysis in Saudi Arabia (SA) is fundamental as it provides a general overview of renal care. The practice of renal dialysis assists in identifying challenges, opportunities, and potential areas for improvement in the provision of the services.ObjectivesThis research investigated the utilization of renal dialysis services in SA by exploring the number of renal dialysis centers, hemodialysis machines (HD), and peritoneodialysis patients.MethodsThe dataset for this study was derived from a collaboration between the General Authority of Statistics (GaStat) and the Ministry of Health (MoH), focusing on indicators for renal dialysis centers and patients across health sectors in 2021. Analysis was conducted using MS Excel 365 and IBM SPSS Version 29, incorporating multiple regression techniques. The health sector was treated as the dependent variable. At the same time, the number of hemodialysis (HD) machines and the counts of HD and peritoneal dialysis patients were considered independent variables.ResultsAround 275 renal dialysis centers, over 8000 HD machines, 20,440 HD patients, and 1,861 peritoneal patients were tallied from two resources. The findings revealed a negative relationship between the health sector and several renal dialysis centers and peritoneodialysis patients, as demonstrated by p < 0.05 in multiple regression analysis.ConclusionThe number of renal dialysis centers influences the availability of HD machines, affecting the number of HD and peritoneodialysis patients. Most national patients preferred MoH over other semi-governmental and private sectors, and vice versa for non-Saudis.

Impact of Anchor Borrowers Programme (ABP) on Rice Production in Ekiti State, Nigeria

This study investigates the impact of the Anchor Borrowers Programme (ABP) on rice production in the Irepodun/Ifelodun Local Government Area, Ekiti State. Structured questionnaires were used to collect data from 150 smallholder rice farmers, with 75 having access to ABP loans and 75 without access. Descriptive statistics, binary logistic regression, multiple regression, and budgetary techniques were employed for data analysis. The return on investment (ROI) for farmers with ABP loans was 2.48, compared to 2.39 for those without, indicating higher profitability for loan beneficiaries. The average age of respondents was 60.3 years, with 87.3% males, 73.3% having formal education, and 62% married. The mean farming experience was 15.9 years, with an average household size of four members and a farm size of 3.8 acres. Significant factors influencing access to ABP loans included age, educational background, and household size (p&lt;0.05). Additionally, educational background, household size, farm size, and access to ABP loans significantly affected net income from rice production (p&lt;0.05). The study concludes that ABP loans positively impact rice production profitability and recommends that more farmers be given access to these loans to enhance rice production in the area.

Efficient Malware Detection in Android Devices to Improve Cyber Security

The project introduces a novel framework for detecting Android malware based on permissions, utilizing multiple linear regression methods. Permissions play a crucial role in the security of the Android operating system, serving as fundamental indicators of an application's behavior. Through static analysis, the framework extracts application permissions and employs machine learning techniques to conduct security analyses. Specifically, the framework employs multiple linear regression techniques to develop two classifiers for permission-based Android malware detection. These classifiers leverage the relationships between various permission attributes to accurately identify potentially malicious applications. Notably, the framework achieves notable performance levels using classification algorithms without the need for overly complex models. In the project, the existing system utilizes the Random Forest (RF) algorithm, while the proposed system adopts the Support Vector Machine (SVM) algorithm. Both algorithms are evaluated in terms of accuracy, with the results demonstrating that the proposed SVM approach outperforms the existing RF method. This highlights the effectiveness of SVM in accurately detecting Android malware based on permission analysis.

Ownership Structure and Financial Reporting Quality of Listed Non Financial Firms in Nigerian: The Moderating Role of Audit Committee

The aim of this study is to evaluate the moderating role of audit committee on the relationship between ownership structure and financial reporting quality of listed non-financial firms in Nigeria. The study used annual reports from 41 listed non-financial firms in Nigeria between 2010 to 2021 period. The study used causal research design approach. The analysis was done using descriptive statistics and multiple regression technique using Generalised Method of Moment (GMM). The findings of the study show that there is significant relationship between ownership structure and financial reporting quality and audit committee can effectively moderate their relationship. The study therefore, rejects all the null hypotheses formulated. The implications of the study are that regulators and policy makers, may encourage more shareholders representation in audit committee of listed companies and mandate financial expertise to all audit committee members. The study concludes that presence of institutional, managerial and foreign ownerships as well as sound audit committee are important for improving firms' financial reporting quality. The study therefore, recommends that in order to enhance the reporting quality, management of listed non-financial firms in Nigeria should ensure that they have effective and functional audit committee. The ownership structure should also be strengthened in order to assist in improving financial reporting quality and also improve relevance and reliability of annual reports and make it easier for the stakeholders to make appropriate decisions relevant to their needs.

Estimating the strength of soil stabilized with cement and lime at optimal compaction using ensemble-based multiple machine learning

It has been imperative to study and stabilize cohesive soils for use in the construction of pavement subgrade and compacted landfill liners considering their unconfined compressive strength (UCS). As long as natural cohesive soil falls below 200 kN/m2 in strength, there is a structural necessity to improve its mechanical property to be suitable for the intended structural purposes. Subgrades and landfills are important environmental geotechnics structures needing the attention of engineering services due to their role in protecting the environment from associated hazards. In this research project, a comparative study and suitability assessment of the best analysis has been conducted on the behavior of the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime and mechanically stabilized at optimal compaction using multiple ensemble-based machine learning classification and symbolic regression techniques. The ensemble-based ML classification techniques are the gradient boosting (GB), CN2, naïve bayes (NB), support vector machine (SVM), stochastic gradient descent (SGD), k-nearest neighbor (K-NN), decision tree (Tree) and random forest (RF) and the artificial neural network (ANN) and response surface methodology (RSM) to estimate the (UCS, MPa) of cohesive soil stabilized with cement and lime. The considered inputs were cement (C), lime (Li), liquid limit (LL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). A total of 190 mix entries were collected from experimental exercises and partitioned into 74–26% train-test dataset. At the end of the model exercises, it was found that both GB and K-NN models showed the same excellent accuracy of 95%, while CN2, SVM, and Tree models shared the same level of accuracy of about 90%. RF and SGD models showed fair accuracy level of about 65–80% and finally (NB) badly producing an unacceptable low accuracy of 13%. The ANN and the RSM also showed closely matched accuracy to the SVM and the Tree. Both of correlation matrix and sensitivity analysis indicated that UCS is greatly affected by MDD, then the consistency limits and cement content, and lime content comes in the third place while the impact of (OMC) is almost neglected. This outcome can be applied in the field to obtain optimal compacted for a lime reconstituted soil considering the almost negligible impact of compactive moisture.

Alignment of Employee Competencies, Information Technology, Accounting Information Systems as the Key to Organisational Performance (Case Study on Manufacturing Company)

This investigation aimed to assess the influence of Employee Competence, Information Technology, and Accounting Information Systems on the performance of CV Nurissah Company. The goal was to enhance the operational outcomes of the organization. The study employed multiple linear regression techniques for data analysis, utilizing the SPSS software for data processing. Data were gathered through questionnaires distributed among 95 staff members at CV Nurissah Company. The findings of this research reveal that (1) employee competence exerts a positive impact on organizational performance; (2) the utilization of information technology enhances organizational performance; (3) the implementation of accounting information systems contributes positively to organizational performance; and (4) the combined effect of employee competence, information technology, and accounting information systems significantly boosts organizational performance.

Factors Affecting Consumers in Making Decisions in Using Robinhood for Food Delivery in Bangkok

The research purpose to study factors affecting consumers in making decisions in using Robinhood for food delivery in Bangkok. By study in factor related and those factors are customer service, quick response, price and characteristics of service provider. Those factors are necessary to study for the need of consumers in making decisions in using Robinhood for food delivery regarding to the selection of service provider. The population samples were the consumer who use Robinhood for food delivery in Bangkok. The selection of sample based on a convenient sampling technique for 400 samples. Questionnaires were used to collect data. Statistic used in data analysis method were percentage, average data, standard deviation. Moreover, testing relationship between independent viable and dependent viable that have multiple independent viable were used by multiple regression technique. Research findings revealed that overall results of factors affecting consumers in making decisions in using Robinhood for food delivery in Bangkok were in high rates. The data of research results, in descending order, revealed that customer service, price, quick response and characteristics of service provider factors affecting consumers in making decisions in using Robinhood for food delivery in Bangkok. Those affected factors had statistically significant at level .05. This research findings were accepted all the research hypothesis that customer service, quick response, price and characteristics of service provider factors affecting consumers in making decisions in using Robinhood for food delivery in Bangkok.

Does Creativity and Product Innovation Build Business Continuity? Investigation of Samarinda Weaving Craft

Abstract In practice, it is often found that the iconic woven sarong products from Samarinda still produce classic models without any new breakthroughs according to tastes, interests across generations, and market segmentation. Until now, another obstacle has been the difficulty of finding skilled artisans to modernize Samarinda’s woven sarongs. Following up on these dynamics, the manuscript aims to explore the causality between creativity and product innovation on the business continuity of the Samarinda woven sarong craft. In the study context, the independent variable is modified to include creativity and product innovation, while the dependent variable is focused on business continuity. To project the effect of creativity and product innovation on business continuity, a purposive sampling approach was associated with 261 sarong-woven craftswomen in Samarinda who still exist today. From the existing interview data, the data is tabulated using a multiple linear regression technique. Quantitative evidence shows that creativity has a significant effect on business continuity in the Samarinda woven sarong craft, but product innovation has an insignificant effect on business continuity in the Samarinda woven sarong craft. The implications for the future can be evaluated based on this research. Then, further theoretical insights can be expanded by exploring factors beyond creativity and product innovation.

Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co(NH3)6]Cl3 complex

AbstractThe study focuses on the efficiency of hexaamminecobalt (III) chloride (HACo, [Co(NH3)6]Cl3) immobilized on activated carbon for removing methylene blue (MB) from water solutions. The primary objective of this study was to assess the sorption performance of HACo immobilized on activated carbon in removing MB from water solutions. Additionally, predictive models were developed to optimize the MB removal percentage. Lastly, the study aimed to determine the optimal conditions for achieving maximum MB removal. Samples were characterized using scanning electron microscopy. Batch sorption experiments were conducted to analyze the impact of MB concentration, adsorbent mass, pH, temperature, and contact time. Predictive models were built using multiple linear regression and neural network techniques, specifically artificial neural networks (ANN) and hybrid ANN–particle swarm optimization (ANN‐PSO). The PSO‐ANN model with a single hidden layer of eight neurons trained using the Levenberg–Marquardt algorithm demonstrated high accuracy in predicting MB removal percentage, with mean absolute percentage error (MAPE) = 0.083788, root mean square error (RMSE) = 0.11441, and R2 = 0.99693. The MB adsorption process followed a mono‐layer with one energy model and a pseudo‐first‐order kinetic model. Optimization using the genetic algorithm revealed that the maximum MB removal percentage of 99.56% is achievable at an MB concentration of 9.36 mg/L, adsorbent mass of 15.72 mg, and temperature of 311.2 K. The study confirms the effectiveness of HACo immobilized on activated carbon for MB removal. The PSO‐ANN predictive model proved superior in accuracy compared to empirical models. Optimization results provide the optimal conditions for maximizing MB removal, offering valuable insights for practical applications.

Experimental investigation of bird dropping and soiling on PV panel power output in a humid and dusty environment

Over the past few decades, the increased use of energy resources has led to an increase in electricity demand, which has compounded a constant shortage of nonrenewable energy resources. However, dust accumulation, humidity levels, temperature, and bird droppings can negatively affect the efficiency of PV systems. Bird droppings can cause shading areas and hot spots on PV cells, gradually reducing their performance. This study aims to investigate the impact of bird droppings and soiling on photovoltaic panels by developing an experimental setup. An experimental setup comprises two PV panels with monitoring and data logging sensors. During the cleaning process, one panel will be compared with a panel affected by bird droppings and soiling. The experiment was conducted for 25 days in a dusty and humid environment, affecting approximately 30% of the PV panel area by bird droppings. As a consequence, the panel's output decreased by 35%. In order to predict when the PV panel becomes completely opaque, resulting in zero power, after the experiment has been conducted, multiple linear regression techniques have been applied that show the whole panel will cover up in 82 days with bird droppings having a covering area of 128500 mm2. The experimental investigation concluded that bird dropping and soiling can significantly impact PV panel output degradation. Regular cleaning patterns will help to ensure efficient operation and maximize electricity generation.

THE INFLUENCE OF THE ATTRACTIVENESS OF PROMOTIONS AND PRODUCT DESIGN ON THE DECISION TO PURCHASE THE HONDA ALL NEW BRIO SATYA

This research aims to analyze the influence of promotional attractiveness and product design on consumer purchasing decisions. The influence of these factors on consumer behavior has become a major concern in modern marketing studies, as they have a significant role in shaping purchasing preferences. In this context, a quantitative approach is used to collect data from respondents consisting of active consumers in various industrial sectors. This research draws samples from a consumer population representing a variety of demographic backgrounds and product preferences. Survey methods are used to collect primary data related to consumer perceptions about the attractiveness of promotions and product designs as well as their purchasing decisions. Data analysis was carried out using multiple linear regression techniques to identify the relationship between the variables studied. The analysis tool uses SPSS 26. The research results show that both promotional appeal and product design have a significant influence on consumer purchasing decisions. These findings provide valuable insights for marketing practitioners to design effective strategies to increase sales and consumer satisfaction.

Navigating new realities: Explaining programmatic transitions of mainstream and niche parties

Electoral competition is determined by the issues that parties choose to compete on and the stances they adopt on these issues. However, little research has examined the trade-off between expanding a party’s programmatic stances to secondary issues while maintaining ideological continuity on primary issues. This article seeks to address this gap by examining programmatic transitions among mainstream and niche parties and in which contexts these transitions are more frequent. The study analyses 47 parties in 10 established democracies between 1986 and 2020 using multiple regression techniques. The results show that niche parties are more likely to focus on secondary issues, and when they make such transitions, they tend to be larger. The analysis also reveals that the length of niche competition influences mainstream parties’ programmatic transitions, while niche parties’ transitions are driven by their continuity in programmatic transitions and governmental experience.

Hectareage Prediction Models for Paddy Crop of Middle Gujarat

The present investigation was undertaken with a view to identify the models for predicting the hectareage of paddy crop of the middle Gujarat region. The investigation was carried out on the basis of secondary data covering the period of nineteen years, (1998-99 to 2016-17). The District level data relating to hectareage, production, productivity and farm harvest prices of paddy were obtained from the published and compiled information by Directorate of Agriculture, Gujarat State, Gandhinagar. The linear multiple regression technique (basically Nerlovian type) was employed. The eight single equation and four simultaneous equation (SE) models were tried for paddy crop, the following models were selected on the basis of the values of adjusted coefficient of multiple determination. SE model-III for paddy is given below.HEPD = 40960.532**** - 10.414*** HEBJ + 0.784 HEMZ - 1.187**** HEPDL + 3.720*** HEBJL + 5.588**** EYPD + 0.866 EYBJ - 6.205*** EYMZ- 6.833**** EPPD + 1.502 EPBJ (R2= 0.946)HEBJ = 3261.298 - 0.061 HEPD + 0.108 HEMZ - 0.093 HEPDL + 0.337 HEBJL + 0.441 EYPD + 0.220 EYBJ - 0.619 EYMZ - 0.594 EPPD + 0.227EPBJ (R2= 0.960)HEMZ = 1816.343 + 0.028 HEPD + 0.147 HEBJ + 0.220 HEBJL + 0.649 HEMZL - 0.120 EYPD - 0.176 EYBJ - 0.092 EYMZ - 0.226 EPMZ - 0.106EPBJ (R2= 0.850)*, **, ***, **** Significant at the 20, 10, 5, 1 percent level of significance, respectivelyFor the selected crops, SE model was recommended for prediction of the current hectareage on the basis of the adjusted coefficient of multiple determination ( R 2). For Paddy hectareage the main affecting factors viz., bajra hectareage, lagged hectareage of paddy, expected yield of maize and expected price of paddy. Expected yield and expected price of paddy were determining factors of bajra hectareage.

Effect of Transformational Leadership Behaviour on Employees’ Career Development in Zenith International Bank (ZIB) Plc, North East 3 Zone, Adamawa State, Nigeria: Post COVID-19 Pandemic Experience

The World Health Organisation (WHO) declared the coronavirus (COVID-19) pandemic as a global pandemic. The pandemic has left organisations with a number of challenges, including decreased revenue, decreased consumer spending, poor marketing outcomes, firm closures, reduced staff motivation, virtual working, increasing unemployment, and changes in service delivery and lifestyle. However, due to the rapid growth in global competition, advancements in technology, innovativeness, and growing demands for better customer service, organisational leaders were challenged to devise performance-improvement strategies in order to survive the challenges posed by COVID-19, particularly in Deposit Money Banks (DMBs). Against this backdrop, this study investigates the impact of transformational leadership (TL) behaviours on employee career development (i.e. employee mentoring and coaching). This study conceptualised TL behaviours as idealized influence, inspirational motivation, intellectual stimulation, and individual consideration, while career development as career coaching and career mentoring. The study used an explanatory survey research approach with a sampl of 130 employees from four (4) branches of Zenith International Bank Plc North East 3 Zone, Adamawa State. This study's data were collected via questionnaire. Descriptive statistics such as mean, standard deviation and correlation were used in analysing the data collected, while study's hypotheses were tested using the multiple regression technique. The regression results show that TL behaviours such as inspirational motivation, intellectual stimulation, and individual consideration have favourable significant effect on career development (Mentoring and coaching). While idealised influence has significant adverse effects on career development in both career mentoring and coaching. Overall, the findings indicate that TL positively and strongly predicts career development (i.e. career mentoring and coaching). As a result, this study concludes that TL actions are critical for organisational development and should be supported by managers since they improve employees' career progression within the organisation and have significant effects on numerous employee outcomes.

Assessment of Organisational Knowledge Management and Innovativeness in Medium-Scale Manufacturing Firms

While the drive towards profit is a distinctive and the most dominating objective of every business, environmental issues are currently putting growing pressure on organisational models. This paper examined the implication of knowledge management- acquisition, distribution, and utilization- on innovativeness in selected manufacturing firms in north-central Nigeria. Data was gathered through the use of structured questionnaire. Multiple regression technique was employed to test the research hypothesis. The study established knowledge management to have a significant effect on organizational innovativeness and recommended that the management of entrepreneurial firms should look at adopting team-focused approaches especially for task implementation to facilitate idea sharing and performance.

The Influence of Earnings Persistence on Financial Sustainability of Commercial Banks in Nigeria

The thought-provoking misdemeanour of some commercial banks nose-diving into a financial crisis, even after presenting seemingly good yearly performance reports, motivated this study. Thus, the study was carried out to examine the relationship between earnings persistence and the financial sustainability of commercial banks in Nigeria. This relationship was examined after controlling for leverage, interest rate and firm size of the commercial banks. The ex-post facto research design was adopted in the study. Data were obtained from the annual reports of 12 selected, listed commercial banks in Nigeria from 2012 to 2022. The multiple regression technique was used to test the hypothesis of the study. The results of the analysis revealed that there is a significant positive relationship between earnings persistence and the financial sustainability of commercial banks in Nigeria (t = 4.318, p = 0.000, p &lt; 0.05). Based on this finding, it was recommended that commercial banks should strive to enhance their earnings persistence by increasing their operational efficiency.

Influence of Big Five Personality Traits on Self-Disclosure in Early Adulthood Instagram Social Media Users

Self-disclosure is the ability of individuals to voluntarily disclose information about themselves to others that is personal. This will affect both him and others, if done excessively. From the influence of self-disclosure factors based on personality type can be seen and focus on the personality of the big five personality in Makassar State University students. This study involved students of Makassar State University (N=381). The sampling technique used is cluster random sampling. The measuring instruments used are the Big Five Inventory scale (BFI) adaptation scale from Ramdhani (2012) and the self-disclosure scale from Tamaraya research (2020). Data analysis used in this study is a multiple linear regression technique. The results showed that there is not significant influence with the four big five personality, namely, Openess to experience, conscientiousness, extraversion, and agreeableness to self-disclosure. While personality neuroticism has a significant effect on self-disclosure. This research has implications in the field of Social Psychology and is useful for students to be able to determine the value of attitudes, emotions, and values that affect individuals on personality that are carried out towards self-disclosure.

Work Climate and Organisational Politics as Predictors of Organisational Commitment among Staff of Benue State University, Makurdi

This study investigated work climate and organisational politics as predictors of organisational commitment among employees of Benue State University, Makurdi. Employing an ex-post facto research design, three hypotheses were formulated, and a sample of 187 participants completed the Organisational Climate Questionnaire (OCQ), Perceived Organisational Politics Scale (POPS), and Organisational Commitment Questionnaire (OCQ). Data analysis involved regression and multiple regression techniques. Hypothesis one, positing that work climate significantly influences organisational commitment among Benue State University staff, was supported (R =.185, R2 =.034, F(1,177) = 6.229, P &lt;.05). Hypothesis two, which suggested that organisational politics significantly affects organisational commitment, was also supported (R =.629, R2 =.396, F(1,181) = 117.960, P &lt;.05). Additionally, Hypothesis three, indicating a joint influence of work climate and organisational politics on organisational commitment, was affirmed (R =.632, R2 =.400, F(2,173) = 56.950, P &lt;.001). Recommendations derived from the findings include prioritising employees commitment to enhance productivity, mitigating favouritism within the organisation to facilitate effective task execution, and empowering managers to assess and improve work climate, thereby fostering a motivating environment conducive to ongoing performance enhancement.

Assessment of knowledge towards first aid skills of epilepsy among undergraduate health students in Riyadh province of Saudi Arabia: A cross-sectional study

BackgroundApplying proper first-aid measures to patients with seizure episodes plays a vital role in preventing the adverse consequences of seizures. Most previous data focused on teachers, healthcare providers and the general public. This study aimed to assess the knowledge of seizure first-aid measures among undergraduate health students. MethodsThis descriptive, cross-sectional study was conducted between May 16 and 31, 2023, with 493 undergraduate health students of Riyadh Province who answered online surveys. The data were collected using the descriptive information form. The chi-square (χ2) test was employed to compare knowledge of seizure first aid between demographic variables. The relationship between knowledge score and independent variables was evaluated using multiple linear regression technique. ResultsOnly 1.6 % of participants demonstrated good knowledge scores toward seizure first-aid measures. The skill most frequently reported was to remove all harmful objects from their vicinity and loosen the tight clothes around the neck during a seizure; 68.7 % of the participants correctly answered with agreed. Conversely, the skill that received the lowest knowledge score among participants was putting a piece object such as a cloth, wallet, or spoon between the teeth to prevent tongue biting during a seizure; only 37.3 % of the participants correctly answered with disagree. Moreover, participants in the advanced age group, medicine and fifth-year, were found to be significant predictors of knowledge and exhibited better knowledge scores toward seizure first aid measures than their peers (p < 0.001). Furthermore, lectures and books were identified (69.6 %) as the most common source of information about seizure first aid. ConclusionThe study concluded that most undergraduate health students demonstrated poor knowledge scores in delivering seizure first aid. This finding suggests that introducing epilepsy education from the first year in all health-related courses is crucial to improving overall awareness and skills in providing seizure first aid.

Prediction of acute toxicity for Chlorella vulgaris caused by tire wear particle-derived compounds using quantitative structure-activity relationship models

Tire wear particles (TWPs) enter aquatic ecosystems through various pathways, such as rainwater and urban runoff. Additives in TWPs can harm aquatic organisms in these ecosystems. Therefore, it is essential to investigate their toxicity to aquatic organisms. In our study, we initially recorded the median effective concentrations of 21 TWP-derived compounds on Chlorella vulgaris growth, ranging from 0.04 to 8.60 mg/L. Subsequently, through an extensive review of the literature, we incorporated 112 compounds with specific toxicity endpoints to construct the QSAR model using genetic algorithm and multiple linear regression techniques, followed by the construction of the consensus model and the quantitative read-across structure-activity relationship (q-RASAR) model. Meanwhile, we employed rigorous internal and external validation measures to assess the performance of the model. The results indicated that the developed q-RASAR model exhibited strong adaptation, robustness, and reliable prediction, with q-RASAR indicators of Q2LOO = 0.7673, R2tr = 0.8079, R2test = 0.8610, Q2Fn = 0.8285−0.8614, and CCCtest = 0.9222. Based on an external dataset containing 128 emerging TWP-derived compounds, the model's applicability domain coverage was 90.6 %. The q-RASAR model predicted that the structure of diphenylamine was associated with higher toxicity, possibly liked to the SpMax2_Bhm and LogBCF descriptors. The established model reliably provides prediction and fills a critical data gap. These findings highlight the potential risks posed by emerging TWP-derived compounds to aquatic organisms.