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Poplars (Populus sp.), a tree that grows rapidly species, are significant as industrial forest products. The delineation and monitoring of poplar cultivated areas are invaluable for decision-making processes. With the remote sensing technology, accurate detection of poplar planted areas could be determined much faster, more economically, and with minimum labor requirements. The objective of this research is to create a map of poplar plantations in the Sakarya region of Turkey utilizing Worldview-3 satellite imagery. Object-based image analysis (OBIA) through the application of the multi-resolution segmentation method (MRS) was employed to generate image segments, and then three prevailing machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF) and Rotation Forest (RotFor) were implemented to produce LULC maps of the study area including 11 landscape features. The most effective and contributing object features that assure high separability between landscape features were determined using a filter-based Chi-square algorithm for the prediction models constructed with SVM, RF, and RotFor classifiers. Results revealed that the SVM classifier achieved the highest overall accuracy (91.73%) with 38 features out of 88 features, about 3% improvement compared to the other algorithms. According to the SHAP analysis, the IHS feature was the most effective one in the constructed RF model, followed by the CI (red edge), NDVI-1 and NDVI-2 vegetation indices.

Landslide susceptibility prediction practices have been increasingly reliant on non-geographically-oriented (i.e., aspatial) machine learning algorithms. While these approaches have exhibited increasing success, they have often faced criticism for their limited consideration of spatial autocorrelations and local variations across geographical space, thereby neglecting the concept of spatial non-stationarity. To fulfill the research gap, this work applies a geographical random forest (GRF) approach, contrasting it with the conventional random forest (RF) algorithm. To this end, the study area, encompassing the Lake Sapanca Basin and its surroundings, was subdivided into 4,452 slope-based mapping units. The effectiveness of both predictive models was then measured by using overall accuracy (OA) and area under the curve (AUC). The results revealed that the GRF (OA = 80.82% and AUC = 85.22%) outperformed the RF algorithm (OA = 75.34% and AUC = 82.50%) by approximately 5% in OA, and demonstrated a 3% improvement in AUC score. The Wilcoxon signed-rank test confirmed significant differences (95% level) between the predictions of both models. The slope parameter emerged as the globally most influential factor, but local interpretations disclosed notable variations in the importance of causative factors contingent upon location. For instance, the curvature parameter was the most important geospatial covariate in around one-third (34.23%) of the slope units, mostly concentrated in the northernmost zones of the study area. On the other hand, elevation was the most important factor for 14.67% of the slope units primarily located in the southern region.

Considering the negative situations faced by the migrating societies, it is known that the language and culture factors exert a stronger influence on the mathematics course than the other undesirable conditions. From this perspective, it is known that the mathematical performance of individuals is likely to increase by placing the concept of number sense in the center. Therefore, the present study aimed to examine the number sense skills of Syrian immigrant students with reference to gender and duration of stay as an immigrant as well as to determine their conceptual awareness of number sense-related components. The descriptive correlational design was used in this study in which 152 immigrant students participated. Web-based two-tiertest was used in the data collection process, and descriptive statistical values and t-test for independent samples in the analysis of the data. As a result, it was found that male students compared to female students, as well as those who had been immigrants in Turkey for a long time compared to those for a shortertime showed higher number sense performances,though most of the students were at a low number sense level. In addition, the differences appeared significantly in relation to the variables of gender and duration of stay as an immigrant. A surprising result is that most students preferred number sense-based solutions as the basis for their answers. It can thus be concluded that the number sense performances of Syrian immigrant students are lower than expected and that future studies are needed not only to focus on teachers but also on teaching methods and materials in order to take immigrant problems under control and to come up with solutions with a view to overcome such challenges.

The aim of this study is to reveal the applications for the tools, methods, and strategies used to provide motivation in online instrument education in line with the opinions of instrument educators. In the study, interview, observation, and literature review, which are among the qualitative research methods, were used. Within the scope of the research, 41 teachers who provided online instrument training were interviewed, and 10-hour lesson observations were made. In the content analysis based on the ARCS model, it was observed that teachers used technological tools and applications to provide motivation in their lessons, included games, and planned their lessons according to age groups and individual differences of students in online instrument education. To provide motivation in the context of the ARCS model, it was seen that they applied the most to establish interest and the least to the satisfaction dimension. Based on this research, it has been proposed to use methods and strategies that motivate students in online courses and to develop online pedagogical approaches and technological course programs.

The term "Almajiri" in Nigeria is used to describe those children or youths who left their hometowns in search for Islamic education. Unlike pre-colonial Nigeria, the "Almajiri" system of education flourished and recorded exceptional accomplishment in literacy and social services, but today the system is faced with a myriad of challenges with glaring abnormalities. The aim of this study was, therefore, to conduct an empirical inquiry into the problems and prospects of the integration programme in North West Geo-Political Zone, Nigeria. A total of 240 "Almajiris"/pupils and 60 "mallams"/teachers responded to questionnaires designed for the study, while 33 community leaders and school heads were interviewed. The study reveals that authorities are performing below expectations in the area of education, which hinders the achievements of quality education for Nigerian "Almajiris." Shortage of qualified teachers in some schools and inadequate numbers of "Almajiri" integrated schools in North West Nigeria were also recorded. The researchers further discovered that the government is not consistent in its policies and programmes there were a lot of irregularities and corrupt practices in institutionalized education, including the "Almajiri" Integrated Programme. It is therefore, recommended that the government should be consistent in its policy and programmes for the betterment of its citizens. Authorities' approach to quality education should be centred on the eradication of corrupt practices. "Almajiri" Integrated Programme should be handled as introduced by president Goodluck's Administration.

ObjectiveGlobally, skull base tumors are among the most challenging tumors to treat and are known for their significant morbidity and mortality. Hence, this study aimed to identify robust associated factors that contribute to mortality of patients following surgical resection for a variety of skull base tumors at the 3-month follow-up period. This in turn helps devise an evidence-based meticulous treatment strategy and baseline input for quality improvement work.MethodologyA retrospective cohort study of patients undergoing skull base tumor resection was conducted at two large-volume neurosurgery centers in Ethiopia. The categorical variables were expressed in frequencies and percentages. Normal distribution of continuous data was checked by histogram and the Shapiro–Wilk test. Median with interquartile range (IQR) was calculated for skewed data, while mean with standard deviation (SD) was used for normally distributed data. Odds ratio and adjusted odds ratio (AOR) were used to express the result of univariate and multivariate binary logistic analyses, respectively. A p-value

In this study, the mediating role of readiness for self-directed learning in the relationship between pre-service teachers' teaching-learning approach and lifelong learning skills was examined. The study group consisted of 800 pre-service teachers studying in different universities in Turkey. Data were collected with three different scales. The analysis of the data was carried out using structural equation modelling. According to the findings obtained and the results of the mediation analysis, it is clear that the readiness of self-directed learning in the relationship between constructivist teaching-learning, which is one of the learning teaching approaches, and lifelong learning tendencies has a full mediating role. However, there was no significant relationship between traditional teaching-learning approach and lifelong learning tendencies and readiness for self-directed learning. As a result, the type of learning-teaching approach affects readiness for self-directed learning and therefore lifelong learning tendencies.

Background/Objectives: Tuberculosis (TB) remains a significant global health challenge, with drug-resistant tuberculosis (DR-TB) posing a greater threat due to difficulty in treatment. This study aimed to investigate the relationship between comorbidities and treatment outcomes in patients diagnosed with DR-TB in rural Eastern Cape using logistic regression. Methods: Data on patient characteristics, comorbidities, and treatment outcomes were extracted from the medical records and analyzed using Python version 3.8. and R version 4.1.1 software. A logistic regression model was used to determine the effects of selected variables on treatment outcomes of DR-TB cases. Results: Hearing loss and hypertension (HTN) were the most frequently observed comorbidities across various DR-TB cases, particularly rifampicin-resistant (RR), multidrug-resistant (MDR), and pre-extensively drug-resistant (pre-XDR-TB) cases. A hearing loss prevalence of 5.8% (26/445) was found among patients receiving treatment for TB, with the intensity of impairment ranging from mild to severe. Gender is significantly associated with the occurrence of HTN among these patients (p-value: 0.022). Comorbidities such as epilepsy, hearing loss, and HTN significantly impact treatment success, with higher risks of mortality and incomplete cure. Using logistic regression, obesity (OR = 3.0884; e = 1.1277; p = 0.0408) and HIV-positive status (OR = 0.4458; e = 0.8078; p = 0.0001) were highly likely and less likely associated with better treatment outcomes, respectively. The logistic regression model achieved an accuracy of 64.0%, a precision of 63.0%, and a recall of 95.0%, with an F-1 score of 76.0%. Conclusions: The findings underscore the importance of implementing integrated management strategies that address both DR-TB and its comorbidities, particularly in resource-limited settings where such patients are prevalent. Public health policies should incorporate strategies to provide nutritional assessments and interventions, particularly for individuals with low BMI. This could include food supplementation programs or partnerships with local food kitchens to ensure that patients have access to adequate nutrition during DR-TB treatment.

Drug-resistant tuberculosis (DR-TB) and HIV coinfection present a conundrum to public health globally and the achievement of the global END TB strategy in 2035. A descriptive, retrospective review of medical records of patients, who were diagnosed with DR-TB and received treatment, was conducted. Student’s t-test was performed to assess differences between two means and ANOVA between groups. The Chi-square test with or without trend or Fischer’s exact test was used to test the degree of association of categorical variables. Logistic regression was used to determine predictors of DR-TB treatment outcomes. A decision tree classifier, which is a supervised machine learning algorithm, was also used. Python version 3.8. and R version 4.1.1 software were used for data analysis. A p-value of 0.05 with a 95% confidence interval (CI) was used to determine statistical significance. A total of 456 DR-TB patients were included in the study, with more male patients (n = 256, 56.1%) than female patients (n = 200, 43.9%). The overall treatment success rate was 61.4%. There was a significant decrease in the % of patients cured during the COVID-19 pandemic compared to the pre-pandemic period. Our findings showed that machine learning can be used to predict TB patients’ treatment outcomes.

This study, which was based on qualitative research design, aimed to examine the research trends and results of bibliometric mapping analyses of articles about the Flipped Classroom Model (FCM) in education in the past ten years. For this purpose, 126 articles published with the keywords; flipped classroom, education, social science, science, chemistry, biology, and physics between the years of 2010-2019 were accessed through the Web of Science database. Out of these 126 articles, 63 were selected using criterion sampling model for the bibliometric and the content analysis. As a result of the bibliometric analysis, it was seen that the most frequently mentioned keywords were flipped classroom, active learning, and chemical education with many of them focusing more on "motivation". EducationThe result of the content analysis showed that the variables included not only "motivation" and "perception" but also "academic achievement/performance" as the most common topics in the studies. The results also revealed that the most commonly used data collecting instruments were achievement tests, questionnaires and scales; the most commonly preferred sample groups were university students; and the most commonly utilized research design was the quantitative method.