Multistage Method Research Articles

Physical Activity Promotion in Schools Using Theoretically Designed Mobile Phone Application.

Regular physical activity for adolescents has many health benefits, many of which also affect adulthood. Physical activity is a behavior that requires planning and choosing an appropriate educational method, model, or theory. However, mobile phone applications are known as a suitable method to increase physical activity according to the guidelines. This study aims to increase physical activity in female students based on the theory of planned behavior using mobile phone applications. This quasi-experimental study was implemented on 220 high school students (110 people in each group). The samples were selected by a multi-stage cluster method and their information was collected by the International Physical Activity Questionnaire (IPAQ) and the Theory of Planned Behavior (TPB) questionnaire whose validity and reliability were confirmed. The intervention group received 8 sessions of education through a mobile phone application that was designed by the constructs of the theory of planned behavior. The samples were evaluated in three stages, through a pre-test, post-test, and two-month follow-up. The results showed a significant difference in the post-test and two months after the intervention between intervention and control groups in terms of attitude, perceived behavioral control, intention, and physical activity. There was a significant difference between intervention and control groups in subjective norms in the post-test, but there was no significant difference in the two-month follow-up compared to the post-test (P = 0.08). An educational intervention based on the theory of planned behavior using mobile phone applications led to an increase in the physical activity of girl high school students. However, to determine the full effectiveness of this study, it is recommended to implement this intervention in all schools.

On the Convergence of Multi-Stage Differential Transform Method

In many paper, Multi-stage Differential Transform Method (MsDTM) has been presented as a method to solve linear and nonlinear equations of various kinds. Multi-stage Differential Transform method has been used to find the exact or analytical approximate solution of the different problems. But only a few works have been considered of convergence of this method. In this paper, Convergence analysis of multi-stage differential transform method is presented. Some numerical examples are proposed to verify the convergence of the proposed method

An Evaluation of Projection Methods for Detailed Small Area Projections: An Application and Validation to King County, Washington.

Population projections are used by a number of local agencies to better prepare for the future resource needs of counties, ensuring that educational, health, housing, and economic demands of individuals are met. Meeting the specific needs of a county's population, such as what resources to provide, where to target resources, and ensure an equitable distribution of those resources, requires population projections which are both demographically detailed, such as by age, race, and ethnicity, and geographically precise, such as at the census tract level. Despite this need, an evaluation of which methods are best suited to produce population projections at this level are lacking. In this study, we evaluate the accuracy of several cohort-based methods for small area population projections by race and ethnicity. We apply these methods to population projections of King County, Washington and assess the validity of projections using past population estimates. We find a clear pattern that demonstrates while simplified methods perform well in near term forecasts, methods which employ smoothing strategies perform better in long-term forecasting scenarios. Furthermore, we demonstrate that model's incorporating multiple stages of smoothing can provide detailed insights into the projected population size of King county and the places and groups which will most contribute to this growth. Detailed projections, such as those provided by multi-stage smoothing methods, enable city planners and policy makers a detailed view of the future structure of their county's population and provide for them a resource to better meet the needs of future populations.

A machine learning driven automated system for safety data sheet indexing

Safety Data Sheets (SDS) are foundational to chemical management systems and are used in a wide variety of applications such as green chemistry, industrial hygiene, and regulatory compliance, among others within the Environment, Health, and Safety (EHS) and the Environment, Social, and Governance (ESG) domains. Companies usually prefer to have key pieces of information extracted from these datasheets and stored in an easy to access structured repository. This process is referred to as SDS “indexing”. Historically, SDS indexing has always been done manually, which is labor-intensive, time-consuming, and costly. In this paper, we present an automated system to index the composition information of chemical products from SDS documents using a multi-stage ensemble method with a combination of machine learning models and rule-based systems stacked one after the other. The system specifically indexes the ingredient names, their corresponding Chemical s Service (CAS) numbers, and weight percentages. It takes the SDS document in PDF format as the input and gives the list of ingredient names along with their corresponding CAS numbers and weight percentages in a tabular format as the output. The system achieves a precision of 0.93 at the document level when evaluated on 20,000 SDS documents annotated for this purpose.

Evaluation of Virtual Learning Environment using DELES questionnaire in Medical Students of Mashhad University of Medical Sciences

Introduction: The environment governing learning is one of the most important influencing factors in education. The outbreak of corona virus (COVID-19) has led to a special attention to E-learning as an aid to break the chain of virus transmission. This study was conducted to determine the status of the learning environment of virtual education of medical students of Mashhad University of Medical Sciences (MUMS) using DELES (The Distance Education Learning Environment Survey) questionnaire. Methods: This cross-sectional study was conducted in the academic year of 2022 at MUMS. Sampling was a multi-stage method. In this way, at first students in different educational levels (basic science, physiopathology, externship and internship) were considered. For gathering data DELES questionnaire was used. Then the link of the tool was shared in internet groups of medical students .Data were analyzed by T-test , ANOVA, Pearson correlation test through SPSS. In all tests p-value was less than or equal to 0.05. Results: A total of 202 medical students with an average age of 22.88±2.48 years were included in the study. The Mean response of participants studied the courses during the semester in the virtual environment was 49.78 ± 27.76 . The participants in the study reported the education situation to be relatively favorable, so that their satisfaction was more in the area of student independence (Relatively favorable) and less in the area of professors' support (poor). The results of the one-way ANOVA test showed that the average satisfaction score of basic science students is significantly higher than that of externship and internship students (P<0.001, F=7.861). Also, the results of the T-Student test showed that there is no statistically significant difference between students in terms of gender and marital status, but non-native students were less satisfied than native students in terms of interactions between students and colleagues (P < 0.03). Conclusion: The attitude of the students towards distance education during the covid-19 epidemic in medical students of MUMS was relatively favorable or relative satisfaction. Therefore, in order to improving the situation of distance education, it is recommended to implement methods to increase the support of professors and improve their communication with students in the virtual education environment.

PIRNet: Personality-Enhanced Iterative Refinement Network for Emotion Recognition in Conversation.

Emotion recognition in conversation (ERC) is important for enhancing user experience in human-computer interaction. Unlike vanilla emotion recognition in individual utterances, ERC aims to classify constituent utterances in a dialog into corresponding emotion labels, which makes contextual information crucial. In addition to contextual information, personality traits also affect emotional perception based on psychological findings. Although researchers have proposed several approaches and achieved promising results on ERC, current works in this domain rarely incorporate contextual information and personality influence. To this end, we propose a novel framework to integrate these factors seamlessly, called "Personality-enhanced Iterative Refinement Network (PIRNet)." Specifically, PIRNet is a multistage iterative method. To capture personality influence, PIRNet leverages personality traits to mimic emotional transitions and generates personality-enhanced results. Then we exploit sequence models to capture contextual information in conversations. To verify the effectiveness of our proposed method, we conduct experiments on three benchmark datasets for ERC, that is, IEMOCAP, CMU-MOSI, and CMU-MOSEI. Experimental results demonstrate that our PIRNet succeeds over currently advanced approaches to emotion recognition.

Characteristics and Healthcare Utilization of Canadians Living With Participation and Activity Limitations (2001-2010): A Population-Based Cross-Sectional Study.

Individuals with participation and activity limitations face important healthcare challenges. We investigated healthcare utilization and characteristics of Canadians living with participation and activity limitations between 2001 and 2010. We pooled data from 5 cycles of the Canadian Community Health Survey (2001-2010 CCHS). The multistage stratified cluster-sampling method used covered approximately 98% of Canadians, aged 12 years and older residing in private dwellings. We described sociodemographic, behavioral, and health-related characteristics of participants with participation and activity limitations and reported their annual utilization (prevalence; 95% CI) of 7 healthcare providers. Multivariable modified Poisson regression identified individual characteristics associated with healthcare utilization and examined the trends over time. Annually, 8.1 million Canadians aged 12 years and older (29.8%) reported participation and activity limitations. Most common health conditions were back problems (37%) and arthritis (34%). Predominant healthcare providers were medical doctors (88.8%; 95% CI = 88.6-89.0), nurses (16.3%; 95% CI = 16.1-16.6), physiotherapists (15.0%; 95% CI = 14.7-15.2), and chiropractors (14.4%; 95% CI = 14.2-14.7). Overall, males, older adults, immigrants, those with lower education, lower income, recent employment, and better general health were less likely to consult providers. Over time, utilization of most non-medical providers increased. Participation and activity limitations are prevalent in Canada, and most consulted medical doctors. Disadvantaged groups reported lower utilization of most providers, emphasizing access challenges and the need for equitable and integrated healthcare policies. Improving access to rehabilitation services and their inclusion within universal healthcare coverage should be a priority.

Multi-stage probabilistic linguistic matching method with the screening mechanism and individual preference relationship fusion

Considering the diversification of transaction information and complexity of matching environment, the original information with the language tendency plays an important role. Thus, under this circumstance, this paper gives a matching decision-making system under the probabilistic linguistic environment so as to improve the matching efficiency and quality. First, this paper demonstrates the multi-stage two-sided matching problem and illustrates the system frame. Then, for the multiple indicators, a probabilistic linguistic integrated cloud Bayesian network is constructed to present the dependency relationship, and determine the corresponding probability. It is known that the accuracy of agents’ preference information acts on the stability of final matching results, which may involve the strong ranking, agents’ psychological preferences and personal interests, etc. Thus, the improved ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French) method is introduced to derive strong ranking and determine preference, indifference, and incomparability relation (PIR). Furthermore, this paper constructs the dynamic two-sided matching model considering the screening effect. Finally, a case study in second-hand house transaction is used to demonstrate the matching process. Simulation and comparison analysis validate its feasibility and effectiveness.

Multi-Stage and Multi-Parameter Influence Analysis of Deep Foundation Pit Excavation on Surrounding Environment

As urbanization accelerates, deep excavation projects have become increasingly vital in the construction of high-rise buildings and underground facilities. However, the potential risks to the surrounding environment and the inherent complexities involved necessitate thorough research to ensure the safety of those engineering projects with deep foundation pit excavation and to minimize their impact on adjacent structures. This study introduces a multi-stage and multi-parameter numerical simulation method to scrutinize the construction process of deep foundation pits. This approach not only investigates the influence of excavation activities on nearby buildings and roads but also enhances the fidelity of simulation models by establishing a three-dimensional finite element model integrated with on-site investigated geological information. Therefore, the proposed method can provide a more holistic and accurate analysis of the overall impacts of the pit excavation process. To examine the feasibility and effectiveness of the proposed method, this study adopts the multi-stage and multi-parameter influence analysis approach for a real practical engineering case to explore the impact of excavation on the foundation pit support structure, nearby buildings, and surrounding roads. The foundation pit support’s maximum displacement was 8.64 mm, well under the 25 mm standard limit. Anchor rod forces were about 10% below the standard limit. Building and road settlements were also minimal, at 10.33 mm and 16.44 mm, respectively, far below their respective limits of 200 mm and 300 mm. This study not only validates the feasibility of design and construction stability of deep foundation pits but also contributes theoretical and practical insights, serving as a valuable reference for future engineering projects of a similar scope.

Penapisan Fitokimia dan Efek Penyembuhan Luka Insisi Daun Jarak Merah (Jatropha gossypiifolia) pada Mencit (Mus musculus)

Bellyache Bush (Jatropha gossypiifolia) is a plant registered as one of the plants withvarious medical properties due to its Flavonoids, Alkaloids, Tannins, and Terpenoids. Bellyache Bush plays a significant role in accelerating wound healing and cell regeneration. This research aims to determine the effectiveness of the ethyl acetate fraction from Bellyache Bush leaves (Jatropha gossypiifolia) on male mice (Mus musculus). Bellyache Bush leaves was extracted using a multistage maceration method using n-hexane and ethyl acetate as solvents. This effectiveness of incision wound healing was tested by divided the test animals into five experimental groups, and each consisting of 3 male mice. All mice were treated according to their respective experimental groups : Group 1 (negative control), Group 2 (positive control with 10% povidone iodine), Group 3 (5% Bellyache Bush extract), Group 4 (10% Bellyache Bush extract), Group 5 (15% Bellyache Bush extract), until the wounds healed or white scar tissue was observed. The results indicate that at a concentration of 15% the ethyl acetate fraction of Bellyache Bush extract was more effective compared to the positive control, povidine iodine.

Development of an efficient vehicle-to-grid method for massive electric vehicle aggregation

The growing adoption of renewable energy and electric vehicles (EVs) has contributed to environmental sustainability; nevertheless, integration of these products into the power grid has become complex owing to their unpredictable nature and variable energy demands. A significant challenge lies in the realization of large-scale, coordinated control of EVs to serve as an alternative to traditional energy storage systems. This challenge is underscored by the complexity of optimization in large-scale cooperative control problems and the difficulty in reducing such problems to an easily manageable and practical level in real-world application. In response to these challenges, a practical mechanism for the integration of EVs into a vehicle-to-grid concept is proposed in this study. In this approach, the constraints involved in merging multiple EVs into a fictitious clustered energy storage unit, which are often neglected, are given renewed focus. An iterative multi-stage optimization method is introduced that includes an EV aggregation clustering model, multi-tier optimization model, and recursive framework. Here, marked efficacy for larger EV fleets is demonstrated for this method, providing optimal charging and discharging schedules for each vehicle while a high degree of precision is maintained. With the proposed technique, validated through numerous case studies using historical data, the global optimum solution is largely approximated, with a marginal deviation of 4 %. In addition, robustness of the model is demonstrated under varying pricing scenarios, with a computation time that increases in a linear manner, rather than exponentially, as the number of EVs increases. Meanwhile, compared with conventional methods, the technique proposed in this study has the capacity to fulfill the charging demands of all users with reduced charging expense, demonstrating the high precision and efficacy of the technique.

Multi-Step Maceration Extraction and Pharmacological Evaluation of Abelmoschus manihot (L.) Medik

A study has been conducted on the phytochemical profile and pharmacological activity of Gedi (Abelmoschus manihot (L.) Medik) using a multistage maceration method. The results of the phytochemical screening test showed that the crude extract of Gedi leaves gave a positive reaction to the presence of alkaloids, tannins, phenols, flavonoids, unsaturated steroids and saponins. The total phenolic analysis showed that the total phenolic concentrations of Gedi leaf extracts in methanol, ethyl acetate, and hexane extracts were 1.70, 1.44, and 0.85mg GAE/g extract, respectively. The results of the antioxidant activity assays obtained an IC50 value for methanol, ethyl acetate, and hexane extracts of Gedi leaves, which were 40.77, 155.50, and 25.84mg/L, respectively. The antimicrobial activity results showed that all three Gedi leaf extracts had activity in inhibiting microbial growth. The inhibitory values for methanol, ethyl acetate, and hexane extracts of Gedi leaves were 7.0, 13.5, and 9.5mm, respectively, for Escherichia coli, and 21.5, 15.0, and 13.0mm, respectively, for Bacillus. The toxicity test results showed that the LC50 values of Gedi leaf extracts for methanol, ethyl acetate, and hexane extracts were 500 ppm, 736ppm, and 2249ppm, respectively.

Explaining the Performance of Nurses in Order to Prevent Nosocomial Infections in Urmia City Hospitals: Application of the Health Belief Model

Background Nosocomial infections (NIs) are one of the medical, social, and economic problems in developing countries and can be prevented. Studying the etiology of NI is one of the most essential activities for designing prevention programs. The purpose of this study was to explain the constructs of the health belief model (HBM) regarding nurses' compliance with standard precautions for the prevention of NI in Urmia hospitals. Methods This cross-sectional study was conducted on 224 nurses of Urmia hospitals who were selected by employing the multi-stage method. The data collection tool was a questionnaire containing demographic characteristics, an HBM questionnaire associated with the prevention of NI, and questions related to the observance of nurses in the field of standard precautions to prevent NI. Data were analyzed using descriptive and inferential statistical methods in SPSS software version 22. Results The results of this study showed the average performance score of nurses in relation to adopting preventive behaviors against NI (71.97 ± 11.08) as acceptable. Also, the results of the study showed that among the constructs of the HBM, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cue to action, and self-efficacy are the most important predictors of adopting preventive behaviors against NI with a variance of 52% among the nurses. Conclusion According to the results of this study, the constructs of self-efficacy, perceived barriers, cue to action, perceived benefits, and perceived intensity are, respectively, the strongest predictors of compliance with standard precautions to prevent hospital infections. Therefore, the results of this study can be useful for health service providers in planning and designing interventions, and determining appropriate strategies for the prevention and control of NI.

Effects of Loading and Compaction Conditions on Permanent Strains of Unbound Lateritic Soils from Senegal and Burkina Faso

Permanent strains of granular materials result from the repetitive passage of heavy trucks on the pavement. Pavement design relies mainly on permanent strains to predict rut depths. In order to contribute to the improvement of technical documents used for the design and modelling of pavements in tropical African countries, the researchers have focused on the determination of the permanent deformation parameters of laterites (axial (εp,1) and radial (εp,3) permanent strains). The Repeated Triaxial cyclic Loading (RTL) apparatus was used according to the European standard (EN 13286-7: 2004, Multistage method). Four sites of gravelly lateritic soils chosen, two from Burkina Faso (Badnogo2 and Dédougou) and two from Senegal (Sindia and Lam-Lam) were tested in this research. They are compacted to 95% and more of the optimum with a variation in water content of ±2% of the optimum. The permanents deformations are influenced by the water content, the compaction rate and the stress level. The axial permanent deformation ε1,p (max) decreases by 83.4% when the water content decreases from 12.10% to 7.59%. Moreover with a compactness rate that varies from 95.58% to 100.06%, respectively from a water content that varies from 10.04% to 9.36%, we observe a decrease in deformation ε_(1,p) from 224.340*10-4 to 148.150*10-4 from the 50,000 cycles, which shows the effect of the compactness rate and the stress level on the lateritic gravels materials soils. however, for a good control of permanent deformations on flexible pavements using gravel materials, it is necessary to compact at water contents lower than the optimum added to average compaction rates. Keywords: permanent strains (p), lateritic soils, Repeated Triaxial cyclic Loading (RTL), Sindia, Badnogo2, Dedougou, Lam-lam.

A multi-stage multi-criterion group decision-making method for emergency management based on alternative chain and trust radius of experts

Multi-criterion group decision-making (MCGDM) is common when a group of experts collectively evaluate feasible alternatives regarding a set of criteria. With the increasing complexity of decision-making activities, there may be intricate interrelationships between criteria. In addition, many MCGDM problems have a dynamic characteristic when alternatives are presented to experts sequentially. In such a multi-stage setting, previous experts have influence on later experts. To overcome these challenges in MCGDM, this study first introduces two concepts, namely, trust radius, to model the social influence of experts, and alternative chain, to represent multiple alternatives that can be coordinated at adjacent stages, and then proposes a multi-stage MCGDM method based on the alternative chain and trust radius of experts. In this method, the weights of experts are determined according to the degree that an expert is influenced by others and the degree that an expert has influence on others. The weights of criteria are determined by the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. This study obtains the optimal alternative chain from a 0–1 optimization model based on comprehensive scores of alternatives at each stage and compatible relationships between adjacent stages. The applicability of the proposed method is demonstrated by an illustrative example regarding emergency management of dammed lakes after natural disasters. Comparative analyses show advantages of this method.

Estimation of sparse covariance matrix via non-convex regularization

Estimation of high-dimensional sparse covariance matrix is one of the fundamental and important problems in multivariate analysis and has a wide range of applications in many fields. This paper presents a novel method for sparse covariance matrix estimation via solving a non-convex regularization optimization problem. We establish the asymptotic properties of the proposed estimator and develop a multi-stage convex relaxation method to find an effective estimator. The multi-stage convex relaxation method guarantees any accumulation point of the sequence generated is a first-order stationary point of the non-convex optimization. Moreover, the error bounds of the first two stage estimators of the multi-stage convex relaxation method are derived under some regularity conditions. The numerical results show that our estimator outperforms the state-of-the-art estimators and has a high degree of sparsity on the premise of its effectiveness.

Association between alternative healthy eating index (AHEI) with metabolic health status in adolescents with overweight and obesity.

There has been lack of evidence on the association between healthy dietary patterns and metabolic health status of adolescents. The present study aimed to evaluate the association between alternative healthy eating index (AHEI) and metabolic health status among a relatively representative sample of Iranian adolescents with overweight/obesity. Adolescents with extra body weight (n = 203, aged 12-18 y), were selected for this cross-sectional study by a multistage cluster random-sampling method. Habitual dietary intakes and diet quality of individuals were assessed using validated food frequency questionnaire and AHEI-2010, respectively. Data on other covariates were also gathered by pre-tested questionnaires. To determine fasting glucose, insulin and lipid profiles, fasting blood samples were collected. Participants were categorized as having metabolically healthy overweight/obesity (MHO) or metabolically unhealthy overweight/obesity (MUO) phenotypes, based on two approaches (International Diabetes Federation (IDF) and combination of IDF with Homeostasis Model Assessment Insulin Resistance (HOMA-IR)). The overall prevalence of MUO was 38.9% (based on IDF criteria) and 33.0% (based on IDF/HOMA-IR criteria). After considering all potential confounders, participants in highest tertiles of AHEI-2010 had lower odds of MUO profile according to both IDF (OR = 0.05; 95% CI: 0.01-0.15) and IDF/HOMA-IR (OR = 0.05; 95% CI: 0.02-0.19) definitions. This association was stronger in adolescents with overweight compared with obese ones and also among girls than boys. Moreover, each unit increase in AHEI-2010 score was associated with lower risk of MUO based on both criteria. Higher adherence to AHEI-2010 was inversely associated with odds of MUO in Iranian adolescents with overweight/obesity.

Toward Generalized Multistage Clustering: Multiview Self-Distillation.

Existing multistage clustering methods independently learn the salient features from multiple views and then perform the clustering task. Particularly, multiview clustering (MVC) has attracted a lot of attention in multiview or multimodal scenarios. MVC aims at exploring common semantics and pseudo-labels from multiple views and clustering in a self-supervised manner. However, limited by noisy data and inadequate feature learning, such a clustering paradigm generates overconfident pseudo-labels that misguide the model to produce inaccurate predictions. Therefore, it is desirable to have a method that can correct this pseudo-label mistraction in multistage clustering to avoid bias accumulation. To alleviate the effect of overconfident pseudo-labels and improve the generalization ability of the model, this article proposes a novel multistage deep MVC framework where multiview self-distillation (DistilMVC) is introduced to distill dark knowledge of label distribution. Specifically, in the feature subspace at different hierarchies, we explore the common semantics of multiple views through contrastive learning and obtain pseudo-labels by maximizing the mutual information between views. Additionally, a teacher network is responsible for distilling pseudo-labels into dark knowledge, supervising the student network and improving its predictive capabilities to enhance its robustness. Extensive experiments on real-world multiview datasets show that our method has better clustering performance than the state-of-the-art (SOTA) methods.

FA‐UNet: Semantic Segmentation of Passive Millimeter–Wave Images for Concealed Object Detection

Passive millimeter–wave (PMMW) scanners are widely used for personal security screening in public places due to their nonradiation and high real‐time capabilities. However, the images obtained by these scanners frequently exhibit low signal‐to‐noise ratios and contrast, presenting challenges for automated detection systems. To address this issue, we propose an efficient semantic segmentation approach, FA‐UNet, that employs a UNet architecture with a fusion attention mechanism to conduct binary classification (human body vs. background, including objects) for PMMW images. This approach incorporates a spatial attention mechanism into the lateral connections between the encoder and decoder and introduces a channel attention mechanism during the feature fusion process in the decoder. By combining these attention mechanisms, FA‐UNet leads to more precise segmentation outcomes. The segmented image is then fused with the original image using our multistage fusion method, in which, first, the two images are blended in a 1:1 ratio for object detection. Then, a new fused image is obtained by adjusting the ratio within a certain range (0.3–0.5). Finally, the object detection results are overlaid on this fused image to generate a directly displayable image. We evaluate our method using a self‐made dataset. Experimental results demonstrate that FA‐UNet can accurately segment the human body region and preserve object shapes effectively. Using the fused image for object detection helps reduce false detections caused by background noise interference while improving the detection rate of weak targets. Additionally, the fused image aids in manual image interpretation in locations with higher security inspection levels and contributes to protect the privacy of individuals undergoing inspection to the greatest extent possible.

Assessing the Level of Oral Health Knowledge and Practice among State University Students in Enugu State, Nigeria.

Background Oral health is often ignored, especially among young adults, posing risks to overall health and well-being. In Enugu State, Nigeria, like every other place in the globe, inadequate oral health knowledge and practices among university students are of great concern. Hence, this study focuses on state university students, recognizing their unique challenges such as study pressures and limited access to oral healthcare. Understanding their oral health knowledge and practices is essential for targeted interventions. Data generate can inform the state governments to prioritize oral health initiatives and allocate resources effectively within the region, addressing a critical public health concern. Aim This study elucidated assessing level of Oral health knowledge and practice among State University students in Enugu State, Nigeria. Methods The sample of the study comprised 786 students drawn through a multistage sampling selection method. A pre-tested, well-structured questionnaire was the main instrument of data collection for assessing level of Oral health knowledge and practice among State University students in Enugu State, Nigeria. Statistical Package for Social Sciences (SPSS) Version 23 was used for analysis. Results Findings showed 34% of students possess inadequate oral health knowledge while lesser percentage of students 1% showed poor oral health practice compared to students’ total population. Whereas socio-economic status indeed plays a role in shaping students’ oral health knowledge and practices, potentially warranting targeted interventions to address disparities and promote oral health equity among university students. However, Chi-square analysis reveals demographic variables’ associations with knowledge and practice levels among students. Gender lacks significance in both areas (p=0.47 for knowledge; p=0.175 for practice). However, age significantly correlates with knowledge (p<0.001), and class level associates highly with both knowledge and practice (p<0.001). Thus, age and class significantly influence students’ habits. Conclusion Relevant oral health educational interventions are required to promote oral health knowledge and practice among State University students in Enugu State, Nigeria.