Random Research Articles

Perceptions of Education Stakeholders on the Effect of Teaching Practices on Academic Achievement in Public Secondary Schools: A Case of Kakamega County, Kenya

The purpose of this study was to determine education stakeholder’s perceptions on the effect of schools’ teaching practices on academic achievement in public secondary schools in Kakamega County, Kenya, The study was guided by School-Based Management Theory. A descriptive research design was used. The target population was 415 principals, 415 Deputy Principals, 415 directors of studies, and 33296 form four candidates of 2022. Stratified and purposive sampling technique was used. The sample comprised of; One hundred principals, 100 deputy principals, 100 directors of studies, and 400 students were chosen as the sample. The research instruments were; Questionnaires, an interview schedule, and document analysis schedule. Descriptive analysis was done using frequencies and percentages while inferential analysis was performed using Chi-square Analysis and Pearson product moment. Qualitative data was transcribed, analysed and reported according to emerging themes The Chi-square analysis indicated a significant difference between schools' teaching practices and academic achievement at a p value p < 0.05. The results indicated that there was a significant difference in their perceptions on the effect of schools’ teaching practices with regard to the effect of the academic achievement with the p value p < .001.The null hypothesis was consequently rejected. The findings indicate a notable disparity in the perceptions of education stakeholders, particularly students and deputy principals, regarding the influence of teaching practices on academic success in public secondary schools in Kakamega County. The p-value of p < .001 signifies a robust level of statistical significance, suggesting that the observed difference in perceptions is improbable to arise from random chance. As a result, the null hypothesis, which posited an absence of difference in perceptions, was dismissed. The evidence indicates a divergence in perspectives between students and deputy principals regarding the influence of teaching practices on academic performance. This may suggest that each group emphasizes or appreciates distinct facets of the pedagogical approaches, or that they possess diverse experiences and insights concerning the implementation of these practices within the educational environment. This implied that that the students and deputy principals perceived differently, the effect of the teaching practices on academic achievement. The study recommends that some teaching practices that were perceived to enhance academic achievement should be used in public secondary schools of Kakamega County.

Energy-Aware Register Allocation for VLIW Processors

AbstractThe efficiency of VLIW processors can be improved by reducing the energy consumption associated with accessing the register-file. This paper presents an energy-aware register allocation approach to reduce the dynamic energy consumption of a scheduled program by using an evolutionary algorithm approach and a register access transition energy model. Additionally, the influence of instruction scheduling on energy-aware register allocation is analyzed. By using the proposed energy-aware compiler backend on synthetic applications with random data in the register-file, the dynamic energy consumption of the total processor core for an exemplary VLIW processor can be reduced by up to $$\varvec{21\%}$$ 21 % compared to a heuristic register allocation. In real-world applications from the domain of hearing aids, energy consumption can be reduced by up to $$\varvec{42.7\%}$$ 42.7 % for single applications, and $$\varvec{17.6\%}$$ 17.6 % average across a range of applications.

Risk Factors for Multidrug Resistance in Patients Infected with Carbapenem-Resistant Klebsiella pneumoniae: A Nomogram.

Our aim was to determine the risk factors for multidrug resistance in patients with carbapenem-resistant Klebsiella pneumoniae (CRKP). The information of 196 patients with Klebsiella pneumoniae infection was collected. The patients were subsequently assigned to the carbapenem-resistant, multidrug-resistant, and non-multidrug-resistant groups. The risk factors for multidrug resistance in CRKP patients were assessed via least absolute shrinkage and selection operator and logistic regression analyses. Moreover, a nomogram was constructed dependent on the identified risk factors, and calibration and decision curves were plotted to detect its accuracy. Length of stay (LOS) [odds ratio (OR) and 95% confidence interval (CI): 4.558 (1.157-17.961), P = 0.030], intensive care unit (ICU) stay within 30 days [OR and 95% CI: 12.643 (3.780-42.293), P < 0.001], Glasgow Coma Scale (GCS) score [OR and 95% CI: 13.569 (2.738-67.236), P = 0.001], fungal infection [OR and 95% CI: 6.398 (1.969-20.785), P = 0.002], and cardiovascular disease (CVD) [OR and 95% CI: 3.871 (1.293-11.592), P = 0.016] were identified as risk factors for multidrug resistance in CRKP patients. The concordance index (C-index) of the constructed nomogram was 0.950 (95% CI: 0.945-0.955). Moreover, decision curve analysis elucidated the nomogram utilization across a wide range of probability thresholds, ranging from 1% to 100%. Finally, internal validation using random data validated the robustness of the predictive model, yielding a C-index of 0.937. The LOS, ICU stay within 30 days, GCS score, fungal infection, and CVD were recognized as risk factors for multidrug resistance in CRKP patients. The constructed nomogram could accurately predict multidrug-resistant CRKP infections in patients.

Image Encryption Using High-Speed Scrambling and ‎Modular Arithmetic

With the enormous growth and advancement of the computer industry, it is critically necessary to transmit a large amount of encrypted multimedia data to prevent third parties from attacking one's privacy or misbehaving. This study proposes a new encryption strategy for preserving original images. It is highly effective and has been shown to be resilient against impulsive noise and loss of data. At first, some random data is entered within the perimeter of the image. Then, three rounds of high-speed scrambling and adaptive diffusion for pixels are carried out to mix the contiguous pixels at random and distribute the newly placed data throughout the image. The suggested encryption method can be used to encrypt original images in any form of representation directly. We offer a particular operation to carry out adaptive diffusion for pixel modulo arithmetic. The results of encryption are evaluated using the histogram, which shows a near-zero correlation with a Number of Pixel Change Rate (NPCR) of 099.624, a Unified Average Change Intensity (UACI) of 033.577, and an average entropy of 7.9993. The proposed method can accomplish much better speed and adapt better to impulse noise and loss of data interference than several conventional and cutting-edge encryption methods.

The Effect of Financial Literacy on the Financial Management of Students

This study aims to determine the effect of financial literacy on financial management of SMA Negeri 1 Ulunoyo students and the magnitude of the effect of financial literacy on financial management of SMA Negeri 1 Ulunoyo students. The method used in this research is quantitative method. The data collection technique used a questionnaire (Likert Scale). Data analysis techniques used to strengthen questionnaire data are validity test, reliability test, correlation coefficient, linear regression, determinant coefficient, hypothesis testing, normality test, multicollinearity test and heteroscedasticity test. This study used 58 respondents with Simple Random Sampling data collection technique. The results of this study indicate that Financial Literacy affects the Financial Management of SMA Negeri 1 Ulunoyo, South Nias Regency by 54.8%, while 45.2% is influenced by factors not in this study. With increased financial literacy, students will increasingly manage their finances wisely, carefully and regularly by applying indicators of each financial literacy variable and financial management variable, so that students can prepare for future needs and finances.

Effectiveness of ethnomathematics-based learning on students’ mathematical literacy: a meta-analysis study

This study aims to determine the effectiveness of ethnomathematics-based learning on students’ mathematical literacy through a meta-analysis study. Primary studies were collected from Google Scholar and ERIC databases based on the inclusion criteria. The results of the literature review obtained 16 studies, either from theses, proceedings, or journal articles. Furthermore, the effect sizes were combined according to the Random Effects Model (REM) and data analysis using CMA v3 and R Studio (Meta Package). The calculation results obtained an overall effect size which indicates that ethnomathematics-based learning has a “strong” effect on students’ mathematical literacy when compared to conventional learning. As a result of the moderator category analysis, it was found that the effect sizes differed significantly according to students’ regional demographics, independent variables, and subject matter. However, it does not show significant differences when based on the duration of the meeting, education level, sample size, experimental design, type and year of publication. Thus, it can be said that ethnomathematics-based learning can be an effective learning model for increasing students’ understanding of mathematical literacy, especially in the Kalimantan Island region, recommended to be integrated with the Ethnomathematics Pop Up Book and Visual Thinking learning model, and Probability & Sets and Transformation Geometry materials, using Nonequivalent Control Group Design are recommended to be taught through this learning model.

A discrete extension of the exponential type II distribution: statistical characterizations, reliability analysis, and Bayesian vs. non-Bayesian inferences for random right-censored and complete count data

A discrete extension of the exponential type II distribution: statistical characterizations, reliability analysis, and Bayesian vs. non-Bayesian inferences for random right-censored and complete count data

Healthcare provider recommendations for COVID-19 vaccination: Prevalence, disparities, and correlates

ObjectiveWe assessed healthcare provider recommendations for COVID-19 vaccination, disparities across sociodemographic factors, and associations with health care coverage, social norms, COVID-19 vaccine hesitancy, and Fox News preference. MethodsWe utilized random sample survey data of Arkansas residents (N = 2201) collected in October 2022 to identify adults with a personal provider who make up the analytical sample of this study (n = 1804). ResultsOver a third (37.2 %; n = 607) of the weighted sample did not receive a recommendation. Adjusted odds of receiving a recommendation were positively associated with health care coverage (aOR=1.66; 95 % CI [1.05, 2.64]) and negatively associated with perceiving "very few” (aOR=0.48; 95 % CI [0.33, 0.72]) or “some but not many” (aOR=0.57; 95 % CI [0.41, 0.80]) rather than “nearly all” people close to them to be vaccinated. Adjusted odds of receiving a recommendation were negatively associated with being very hesitant (vs. not at all hesitant) (aOR=0.65; 95 % CI [0.47, 0.88]). ConclusionProvider recommendations for COVID-19 vaccination were not provided for a large portion of Arkansas adults, were inconsistently provided across sociodemographic groups, and were associated with health care coverage, social norms, and vaccine hesitancy. Practice implicationsIntervening on disparities in COVID-19 vaccination may require addressing disparities in provider recommendations.

Proportion of Breast Self Examination Practice and its Associated Factors among Female Health Professionals in Government Medical College Hospital , Thiruvananthapuram.

Breast cancer is a leading cause of death among females across the globe. Breast self- examination is considered as a cornerstone testing method for breast cancer screening and early detection. Breast Self Examination is one of the simple, quick and cost free procedures for early detection of breast cancer among women. The Present study was intended to estimate the proportion of Breasts self examination practice and its associated factors among female health professionals in Government Medical College Hospital, Thiruvananthapuram. The research design adopted for this study was descriptive cross sectional design. The sample consists of 330 female health professionals. Sample fulfilling the eligibility criteria were selected through simple random sampling method and data were collected by giving questionnaire. The collected data was analyzed by using SPSS version 23 and the results were expressed in descriptive and inferential statistics. The collected data showed that about 68% of the participants were doing breast self examination practice and there was a significant association between breast self examination practice and associated factors such as age, basic qualification, highest qualification, profession, years of experience, screening for breast cancer and knowledge and attitude regarding breast self examination practice.The study concluded that adressing all these factors can improve breast self examination practice among female health professionals.

Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition

Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water level, and displacement deformation shows a step-like curve. Landslide displacement in TGRA is related to its geology and is affected by external factors. Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimized kernel extreme learning machine (HHO-KELM). Specifically, VMD decomposes the measured displacement into trend, periodic, and random components. Then, the influencing factors are also decomposed into periodic and random components. The feature data, with periodic and random data, are input into the training set, and the trend, periodic, and random term components are predicted by HHO-KELM, respectively. Finally, the total predicted displacement is calculated by summing the predicted values of the three components. The accuracy and effectiveness of the prediction model are tested on the Shuizhuyuan landslide in the TGRA, with the results demonstrating that the new model provides satisfactory prediction accuracy without complex parameter settings. Therefore, under the premise of VMD effectively decomposing displacement data, combined with the global optimization ability of the HHO heuristic algorithm and the fast-learning ability of KELM, HHO-KELM can be used for displacement prediction of step-like landslides in the TGRA.

A remark on randomization of a general function of negative regularity

In the study of partial differential equations (PDEs) with random initial data and singular stochastic PDEs with random forcing, we typically decompose a classically ill-defined solution map into two steps, where, in the first step, we use stochastic analysis to construct various stochastic objects. The simplest kind of such stochastic objects is the Wick powers of a basic stochastic term (namely a random linear solution, a stochastic convolution, or their sum). In the case of randomized initial data of a general function of negative regularity for studying nonlinear wave equations (NLW), we show necessity of imposing additional Fourier–Lebesgue regularity for constructing Wick powers by exhibiting examples of functions slightly outside L 2 ( T d ) L^2(\mathbb {T}^d) such that the associated Wick powers do not exist. This shows that probabilistic well-posedness theory for NLW with general randomized initial data fails in negative Sobolev spaces (even with renormalization). Similar examples also apply to stochastic NLW and stochastic nonlinear heat equations with general white-in-time stochastic forcing, showing necessity of appropriate Fourier–Lebesgue γ \gamma -radonifying regularity in the construction of the Wick powers of the associated stochastic convolution.

Are drivers recurring or ephemeral? observations from serial mapping of persistent atrial fibrillation.

Rotational re-entries and ectopic foci, or 'drivers', are proposed mechanisms for persistent atrial fibrillation (persAF), but driver-based interventions have had mixed success in clinical trials. Selective targeting of drivers with multi-month stability may improve these interventions, but no prior work has investigated whether drivers can be stable on such a long timescale. We hypothesized that drivers could recur even several months after initial observation. We performed serial electrophysiology studies on paced canines (n = 18, 27-35 kg) at 1-, 3-, and 6 months post-initiation of continual persAF. Using a high-density 64-electrode catheter, we captured endocardial electrograms in the left atrium (LA) and right atrium (RA) to determine the presence of drivers at each major anatomical site. We defined drivers that were repeatedly observed across consecutive studies to be recurrent. The mean probability that any driver would recur was 66% (LA: 73%, RA: 41%). We also found evidence of 'multi-recurring' drivers, i.e. those seen in all three studies. Multi-recurring drivers constituted 53% of initially observed drivers with at least one found in 92% of animals, and we found more multi-recurring drivers per animal than predicted by random chance (2.6 ± 1.5 vs. 1.2 ± 1.1, P < 0.001). Driver sites showed more enhancement than non-drivers during late gadolinium enhancement-magnetic resonance imaging (P = 0.04), but we observed no relationship between enhancement and driver recurrence type. We observed recurring drivers over a 6-month period at fixed locations, confirming our hypothesis. We also found drivers to be associated with fibrosis, implying a structural basis.

Interpretable machine‐learning models for predicting creep recovery of concrete

AbstractCreep recovery of concrete is essential for accurately assessing the performance of concrete structures over service time. Existing creep recovery models exhibit low accuracy, and the influencing factors of creep recovery remain inadequately elucidated. In this paper, interpretable machine learning (ML) techniques were employed to develop a prediction model for concrete creep recovery. Several ML techniques were selected including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM). In order to maximize the sample size of the dataset, 109 sets of creep recovery data were collected from existing literatures for model training. Feature selection is utilized to determine the input parameters for ML models, and 12 input variables were selected. The model is fine‐tuned using Bayesian optimization techniques. To ensure the reliability of ML models, 10‐fold cross‐validation and random data splitting were implemented. The results indicate that the ML models exhibited higher accuracy compared to the existing creep recovery model. Among these ML models, LGBM demonstrated superior accuracy, efficiency and stability (with R2 = 0.993, 0.978, and 0.973 for the training, testing, and validation sets, respectively). Shapley additive explanations (SHAP) were employed to interpret the significance of each input parameter on ML model prediction. Duration after unloading, stress magnitude, and ambient relative humidity were the main feature variables influencing concrete creep recovery. Upon comparing the influencing factors, it was discerned that there exists a distinct difference between creep and creep recovery of concrete.

A simple activity to introduce matched pairs and learning effect

AbstractThis paper describes an in‐class activity to introduce random assignment, paired data, and learning effect. The activity requires minimal materials, can be completed in a single class period, and is suitable for those using technology to conduct data exploration but can also be adapted for use in a technology‐free classroom. The activity consists of students filling in two lines of circles, one using their dominant and the other using their nondominant hand. The difference in time to fill in the circles is compared. Results typically show a significant difference in time to fill in circles with the nondominant hand taking significantly longer, a significant learning effect, and a difference in time to fill in circles using a pencil compared to pen.

Improving neural-network-based prediction models for misalignment in off-axis three-mirror anastigmat telescopes

The performance of a telescope system heavily relies on the precise alignment of the mirrors. The off-axis three-mirror anastigmat (TMA) telescope presents unique challenges due to its complex optical design. Each optical element within the off-axis TMA telescope is inherently introduced with theoretical eccentricity and tilt. Furthermore, the incorporation of freeform surfaces and other optical elements with intricate surface features typically leads to low initial alignment accuracy of the optical path. With this low initial alignment accuracy and the noise of the measurements, the prediction of the misalignment of the telescope is getting harder. A fully connected neural network architecture is proposed as a misalignment calculation method for an off-axis TMA telescope system with a freeform surface. Random training data is created by using optical design software. The sensitivity of the mirrors to the wavefront error is quantified and incorporated into the loss function of the neural network to improve prediction accuracy. Adding the noisy measurement samples to the training data creates a noise-immune neural network model. Simulation results show that our model can successfully predict misalignment of the mirrors with noisy measurement values.

Predicting criminal offence in adolescents who exhibit antisocial behaviour: a machine learning study using data from a large randomised controlled trial of multisystemic therapy.

Accurate prediction of short-term offending in young people exhibiting antisocial behaviour could support targeted interventions. Here we develop a set of machine learning (ML) models that predict offending status with good accuracy; furthermore, we show interpretable ML analyses can complement models to inform clinical decision-making. This study included 679 individuals aged 11-17 years who displayed moderate-to-severe antisocial behaviour, from a controlled trial of Multisystemic-therapy in England. The outcome was any criminal offence in the 18 months after study baseline. Four types of ML algorithms were trained: logistic regression, elastic net regression, random forest, and gradient boosting machine (GBM). Prediction models were developed (1) using predictors readily available to clinicians (e.g. sociodemographics, previous convictions), and (2) with additional information (e.g. parenting). Model agnostic feature importance values were calculated and the most important predictors identified. Nested cross-validation with 100 iterations of random data splits and 10-fold cross-validation within each iteration was employed, and the average predictive performance was reported. Among the ML models using readily available predictors, the GBM is the strongest model (AUC 0.85, 95% CI 0.85-0.86); the other models have average AUCs of 0.82. This performance was better than using only the total number of previous offences as the predictor (0.67, 0.66-0.68), and the model simply assuming past offending status as the prediction (0.81, 0.80-0.81). Additional predictors slightly increased the performance of logistic regression and random forest models but decreased the performance of elastic net regression and gradient boosting machine-based models. The potential utility of ML approaches for accurately predicting criminal offences in high-risk youth is demonstrated. Interpretable ML-based predictive models could be utilised in youth services or research to help develop and deliver effective interventions.

Sample size for assessing a diagnostic accuracy of AI-based software in radiology

Introduction. Determining the minimum sample size for solving various tasks is an extremely important and at the same time unexplored problem. There are many methods, but most of them are not applicable for AI-based software validation.Aim: To consider a methodology for determining a balance of classes “norm”/ “abnormality” and propose a statistical approach to determine the data amount necessary for testing AI-based software (validation).Material and Methods. The results of AI-based software were analyzed using dataset of mammograms. Mammograms were classified by the presence of breast cancer (“abnormality”) and the absence of breast cancer (“norm”). The general set contains 123,301 unique studies. The original balance of classes in the study was “norm” 89.3%/“abnormality” 10.7%. As the results of AI-based software (ML-algorithm), a probability of the presence of pathology in the entire study was taken. The following values were used as empirical data (GT): 0 – in case of Bi-RADS classes 1 or 2 diagnosed by a doctor, and 1 – in case of Bi-RADS classes 3, 4, 5. Each data sample is transferred to AI-based software for processing. Quality metrics are calculated based on its results: AUC ROC. All the described actions were repeated 10,000 times for all the studied balances of “norm”/”abnormality”. Based on the results of AUC ROC calculations, mean values were calculated for different random data series with the same balances. Mean AUC ROC values were subjected to analysis.Results. A maximum value of the coefficient of variation of AUC ROC values for 10% “abnormality” share is achieved at the number of studies equal to 190; for the 20% share, it is 80 studies; for the 30% share – 120 studies, for the 40% share – 110 studies, and for the 50% share – 70 studies.Conclusion. Summarizing the conducted study results, it can be concluded that when testing AI-based software, it is necessary to consider that the number of studies reflecting the greatest heterogeneity of AUC ROC values (the largest deviation from the mean value) is different for various class balances. If the purpose of validation is to establish the worst-case behavior of AUC ROC values, then for the studied AI-based software, the “abnormality” share should be 10%, and the number of studies 190. If the validation is carried out under conditions of a limited amount of data, then the “abnormality” share should be 50% and the number of studies equal to 70.

A shaping two-stage anomaly data recovery method based on multi-norm joint optimization under energy internet

With the deep integration of energy networks and information communication technology, the involvement of hardware and environmental factors intensifies the challenges to data security. Prior research focuses predominantly on interpolating random missing data under the assumption of no data damage, without providing strategies for addressing continuous data loss. To overcome, we proposes a novel two-stage data recovery method, that includes three-fold ideas: (1) it introduces the matrix shaping differentiation strategy to realize the differentiated recovery of random and continuous missing data; (2) it designs a two-stage data recovery approach, in which the first stage constructs a random missing data recovery objective function by eliminating the consecutive missing data columns, and the second stage performs temporal pre-interpolation backfilling on the eliminated columns and introduces the temporal correlation to construct the temporal smoothing objective function; and (3) it develops an alternating direction method of multipliers-based iterative solution algorithm. Experimental results on four public datasets demonstrate that our model is superior to five state-of-the-art and classical methods in terms of random missing error rate, random column missing error rate, root mean square error, and mean absolute error.

AI discernment in foot and ankle surgery research: A survey investigation

BackgroundThis study evaluated the ability to differentiate between AI-generated and human-authored abstracts in foot and ankle surgery. MethodsAn AI system (ChatGPT 3.0) was trained on 21 published abstracts to create six novel case abstracts. Nine foot and ankle surgeons participated in a blinded survey, tasked with distinguishing AI-generated from human-written abstracts, rating their confidence in their responses. Surveys were completed twice at two different time points to evaluate intra-/inter-observer reliability. ResultsThe overall accuracy rate for distinguishing AI-generated from human-written abstracts was 50.5 % (n = 109/216), indicating no better performance than random chance. Reviewer experience and AI familiarity did not significantly affect accuracy. Inter-rater reliability was moderate initially but decreased over time, and intra-rater reliability was poor. ConclusionsIn their current form, AI-generated abstracts are nearly indistinguishable from human-written ones, posing challenges for consistent identification in foot and ankle surgery. Level of evidenceIV

Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction.

Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.