Publication Management System
Not a member yet
3916 research outputs found
Sort by
Adapting and Validating a Motor Intelligence Assessment Tool for Children with Intellectual Disabilities: Prioritizing Movement and Sensory-Motor Integration
Get PDFBackground: Motor intelligence, which involves the integration of sensory input and motor output, plays a crucial role in the physical, cognitive, and social development of children with intellectual disabilities (ID). While validated tools exist to measure motor intelligence in typically developing children, there is a significant gap in reliable and adaptable assessments for children with ID. Assessing motor intelligence in this population is essential for identifying sensory-motor deficits and designing targeted interventions to enhance physical performance, promote participation in physical activities, and improve overall quality of life.
Objective: To evaluate the reliability, validity, and sensitivity of the adapted tool in identifying sensory-motor deficits and movement priorities specific to this population. The ultimate goal is to provide a practical and effective assessment tool that can inform targeted interventions to improve motor performance, physical activity participation, and overall developmental outcomes for children with ID.
Methods: A total of 100 children aged 9–12 years with mild-to-moderate intellectual disabilities (IQ range 50–70) were randomly selected from a special education school in Assiut province, Egypt. The study adapted an existing motor intelligence test battery, originally designed for typically developing children, to better suit the sensory-motor and cognitive abilities of children with ID. The adapted battery included tasks evaluating sensory-motor coordination, balance, motor planning, and movement prioritization. Modifications were made to simplify instructions, reduce task complexity, and incorporate visual and auditory cues to accommodate the unique needs of children with ID. Reliability and validity were assessed using Pearson’s correlation coefficients and t-tests, while factor analysis was conducted to identify key dimensions of motor intelligence in this population.
Results: The motor intelligence test battery demonstrated high reliability (r = 0.813 to 0.999) and validity (t-values ranging from 7.98 to 9.33; p < 0.01). Tasks such as "Consecutive Jumps" (r = 0.980) and "Sound and Motion" (r = 0.915) showed excellent reliability, indicating their suitability for children with ID. However, tasks requiring more complex coordination, such as "Rolling Ball," exhibited moderate reliability (r = 0.529), suggesting the need for further refinement or alternative task designs for this population. Factor analysis revealed five distinct dimensions of motor intelligence, collectively explaining 35.65% of the variance, which aligned with the movement priorities and sensory-motor challenges specific to children with ID. Standardized score tables were developed to ensure fair and accurate interpretation of test results, accounting for the variability in motor abilities within this population.
Conclusion: The adapted motor intelligence test battery proved to be a reliable and valid tool for assessing motor intelligence in children with intellectual disabilities. The modifications made to the original test battery ensured its appropriateness for this population, enabling the identification of sensory-motor deficits and movement priorities. The study highlights the importance of tailoring assessment tools to the unique needs of children with ID, ensuring accurate measurement and meaningful interpretation of results. The researcher recommends the inclusion of the adapted motor intelligence battery and the standardized score tables in related programs within intellectual schools to support the development of targeted interventions. These interventions can enhance motor performance, promote physical activity participation, and improve overall quality of life for children with ID
Analysis of Occupational Health and Safety Risk Management: Hazard Identification, Risk Assessment, and Risk Control-HIRARC for Workers at Health Quarantine Offices in Makassar, Indonesia
Get PDFWork hazards and risks are closely related to occupational activities and have the potential to cause injuries and occupational diseases. Every workplace carries the risk of accidents, as reflected in data from Indonesia's Work Accident Insurance Program (JKK BPJS Ketenagakerjaan). The number of workers experiencing fatalities due to occupational accidents and diseases decreased from 4,007 cases in 2019 to 3,410 cases in 2020 but increased again to 6,552 cases in 2021. This study aims to assess occupational health and safety risk management using the Hazard Identification, Risk Assessment, and Risk Control (HIRARC) method among workers at the Makassar Health Quarantine Center. This descriptive study involved a population of 133 workers, with a sample of 57 workers selected using simple random sampling. Data were collected using the HIRARC questionnaire and analyzed using univariate analysis. The results showed that the majority of respondents were aged 40–49 years (57.9%), and 73.7% worked more than 8 hours per day when assigned to night shifts. The HIRARC assessment identified that the most common occupational hazard experienced by workers was ergonomic risk, with complaints of back, waist, and shoulder pain, classified as a moderate risk. In conclusion, ergonomic hazards pose a significant issue among workers, categorized as a moderate risk level. Therefore, it is recommended that the Makassar Health Quarantine Center enhance its occupational health and safety risk management and conduct regular evaluations of workplace hazards and risks
Psychological Variables as Correlate of Special Olympic Sports Participation among Persons with Intellectual Disability in Lagos State, Nigeria
No full textIntroduction: The enthusiasm to participate in Special Olympic sports activities by persons with Intellectual Disability (ID) in Lagos, Nigeria, has been reduced. This research work, therefore, focused on psychological factors and Special Olympic sports participation among persons with mild Intellectual Disability (ID) in Lagos state, Nigeria.
Method: Three research hypotheses were formulated to guide the study. A descriptive survey design was adopted; The study participants included 290 parents of persons with mild ID in Lagos state, Nigeria. Purposive and census sampling techniques were used to select the sample. A self-designed research instrument titled Psychological Factors and Sport Participation among Persons with Mild Intellectual Disability Questionnaire (PFSPPMIDQ) was used for data collection in this study. Simple linear regression was used as a tool for data analysis.
Result: It was discovered that motivation, anxiety, and stress significantly influence sports participation among persons with mild ID in Lagos state, Nigeria.
Conclusion: The study highlights the substantial effect of psychological factors like motivation, anxiety, and stress on Special Olympic sports participation in persons with mild intellectual disabilities in Nigeria. The findings stress the need for focused interventions to enhance motivation and alleviate anxiety and stress to improve participation levels. Special Olympics organizers and sports managers should implement strategies to create a more supportive and encouraging environment, ensuring increased engagement and participants' overall well-being
Predictive Model of Stunting in Children 6-59 Months of Age in Kirundo Health District, Burundi
No full textAn analytical cross-sectional study was conducted among a randomly selected sample of 374 households with at least one child aged 6 to 59 months in the Kirundo health district, Burundi. Sociodemographic, socioeconomic, socio-sanitary factors, food insecurity, behavioral, and environmental data were collected using a structured questionnaire. Children's weight was measured using a standard procedure (SECA scale), their height using a standard UNICEF height rod, and their age was obtained from the birth certificate. Anthropometric data were analyzed using Emergency Nutrition Assessment (ENA for Smart) software.
Modeling was performed using logistic regression to eliminate confounding factors, and all independent variables with a significance level less than or equal to 20% in the bivariate analysis were included to explore factors associated with stunting in children aged 6 to 59 months.
In this study, the prevalence of stunting is estimated at 61.5%. According to multivariate logistic regression, sex (AOR = 2.83; 95% CI:1.40-5.75), age (AOR= 10.40; 95% CI: 1.21-88.30), food insecurity (AOR = 10.47;95% CI: 3.58-30.61), latrine type (AOR = 6.83; 95% CI: 3.12-14.94), diarrhea (AOR = 2.56; 95% CI: 1.19-5.48), water source (AOR = 3.17; 95% CI: 1.54-6.52), media exposure (AOR = 0.24, 95% CI: 0.11-0.51), nutritional knowledge (AOR = 0.11; 95% CI: 0.05-0.25), birth spacing (AOR = 0.39, 95% CI: 0.16-0.93), complete vaccination (AOR = 0.06; 95% CI: 0.02-0.21), father's occupation (AOR = 0.25; 95% CI: 0.09-0.72), and mother's education (AOR = 0.21; 95% CI: 0.07-0.64) were significantly associated with stunting.
The predictive model showed an area under the curve (AUC) of 0.95, indicating excellent discrimination ability.
The high prevalence of stunting in this study highlights the importance of urgent action to end this problem
Machine Learning-Based Maternal Health Risk Assessment: A Comparative Analysis of Classification Algorithms for Predicting Risk Levels During Pregnancy
Get PDFBackground: Maternal health risk assessment remains a critical challenge in healthcare, particularly in resource-limited settings where early identification of high-risk pregnancies can significantly impact maternal and fetal outcomes. This study evaluates the performance of multiple machine learning algorithms for predicting maternal health risk levels using physiological parameters.
Methods: We analyzed a dataset of 1014 pregnant women from Kaggle, incorporating six key features: age, systolic blood pressure, diastolic blood pressure, blood sugar levels, body temperature, and heart rate. Risk levels were classified as mild (0), moderate (1), and severe (2). Four machine learning algorithms were implemented and compared: Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN).
Results: Random Forest and SVM achieved perfect classification performance with 100% accuracy, precision, recall, and F1-scores across all risk categories. Logistic Regression demonstrated strong performance with 98% overall accuracy, showing minor challenges in recall for moderate risk cases (93%). KNN achieved 98% accuracy with balanced performance across risk categories, though slightly lower precision for mild risk cases (95%).
Conclusion: Machine learning algorithms, including Random Forest and SVM, show promise in predicting maternal health risks; however, further validation across diverse populations is essential before clinical adoption
A New Robust Imputation Method for Longitudinal Data with Non-Normal Continuous Outcomes
Get PDFMissing values is very common in longitudinal data and it is the main challenge in analysis of longitudinal data. Missing values have a significant effect on longitudinal data analysis because they lead to loss of information, biased estimates, and misleading results. In practice there is a need for an imputation method to deal with missing values.
Aim: In this study a new robust regression-based imputation method to deal with missing values in longitudinal data is proposed. This method utilizes the modified adaptive linear regression model and does not require the normality of the responses. It is a novel robust imputation method as it is introduced for the first time.
Results and Conclusion: The simulation results show that the proposed method performs well compared to other methods especially for multivariate t-distribution and Chi-square distribution. Also, the proposed approach is effective apart from the missingness rate
Validating Medical Treatment Effects by Projected F-tests under High Dimension with a Small Sample Size
Get PDFThis paper introduces a statistical method for validating treatment effects in high-dimensional medical data with small sample sizes. The method compares multiple multivariate population means under multivariate normality, using spherical matrix distribution theory and principal component analysis (PCA) for dimension reduction. The resulting test statistic follows an exact F-distribution under the null hypothesis of equal means, even when the sample size is smaller than the data dimension. Unlike classical MANOVA, the approach does not require equal covariance matrices across groups, making it more robust for real-world biomedical data where variance-covariance homogeneity rarely holds. Monte Carlo simulations show the test achieves accurate type I error control and favorable power. Application to real medical datasets with high-dimensional biomarkers further demonstrates its practicality and interpretability. This work provides a rigorous and versatile advancement for high-dimensional inference in biomedical research and related fields
Dataset-Specific Bootstrap-Stability Weighting for Calibrated and Clinically Useful Ensemble Prediction in Medical Diagnosis
Get PDFBackground: Ensemble machine-learning models often perform well within a single medical dataset yet lose discrimination, calibration, and decision usefulness under dataset shift.
Objective: To develop and evaluate Bootstrap-Guided Optimization System (BOOTMED), a bootstrap-guided framework that learns dataset-specific weights from resampling stability to fuse probabilistic predictions, targeting discrimination, calibration, and decision-analytic utility simultaneously.
Methods: Four heterogeneous UCI medical datasets were analyzed (Chronic Kidney Disease; CKD, diabetes, heart disease, breast cancer). Base learners were k-nearest neighbors, random forest (RF), Gaussian naïve Bayes, and complement naïve Bayes. BOOTMED estimated stability-derived weights over 500 bootstrap resamples and aggregated model probabilities. Performance was compared with equal-weight voting and stacking using balanced accuracy and ROC-AUC, calibration error (Brier/ECE), and decision curve analysis.
Results: BOOTMED outperformed equal-weight voting and the best single model across all datasets, improving balanced accuracy by approximately 0.7-2.3 percentage points (adjusted p<0.05). Calibration error decreased (lower Brier/ECE), and decision curve analysis showed consistent positive net benefit across clinically relevant thresholds (0.10-0.50). Transferring weights between datasets reduced performance, supporting dataset-specific optimization.
Conclusion: Bootstrap-guided, dataset-specific weighting can improve discrimination, calibration, and clinical net benefit across heterogeneous medical datasets, offering a simple and reproducible ensembling strategy for diagnostic prediction
Axial Performance of Steel Piles in Sand and their Implications for Polymer-Based Coatings, Composite Strengthening, and Soil–Polymer Interaction Systems
Get PDFPolymer-based coatings, composite wraps, and functional polymer interfaces are increasingly used to enhance the durability and axial performance of steel piles in infrastructure applications. The establishment of baseline behavior of uncoated piles is a prerequisite for the design of integrated polymer systems. This paper presents an experimental comparison of the axial performance of H-section steel and closed-ended pipe piles embedded in poorly graded sand (SP) at 58% relative density. Eighteen static load tests were carried out on single piles and 2-pile and 4-pile groups using L/D ratios of 10 and 15. H-piles consistently demonstrated higher ultimate capacities because of soil plug formation, better interface shear mobilization, and densification during driving. Capacity gains with increasing L/D were as high as 109%, and up to 365% in H-pile groups. The test results establish a benchmark dataset for developing polymer-coated, polymer-modified, and FRP-strengthened pile systems and contribute to advances in polymer applications in geotechnical and infrastructure engineering
A Hoplological and Interdisciplinary Perspective on the Lechitic Roots of Today's Poles and Poland
Get PDFProblem: There are ongoing disputes over the origins of Western Slavs and Poles. The author attempts to describe the state of knowledge in light of recent research and analyzes the content of the new scientific monograph ”About military and physical culture in the lands of Lechia.” He does so with an emphasis on the heritage of military culture.
Method: The results of archaeogenetic and hoplological research, as well as an analysis of the relevant literature, are considered together. Logical arguments are used.
Results: An approach synthesizing the current state of knowledge, presented in the monograph, is described. This particularly falsifies the hypothesis of the late arrival of the Slavs in Central Europe. The analysis of the author's monograph is supplemented with new observations regarding weapons and other attributes of legendary heroes and demigods.
Conclusions: The monograph provides a series of logical proofs of the presence of Slavs, known for example as the Veneti, in Polish lands since at least 2000 BCE, which refutes the hypotheses of a late arrival of Slavs in the Lechitic lands. Genetic studies have demonstrated the continuity of settlement of these lands by the direct ancestors of today's Poles. Similarities have been observed in weaponry and military culture