Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

A man in his 60s with multiple vascular comorbidities presented with sudden, painless vision loss in one eye. Although he had a high risk for atherosclerotic events, initial evaluation for stroke was negative for acute ischemia, but found to have markedly elevated inflammatory markers. Accordingly, giant cell arteritis was investigated and Ophthalmologic findings and fulfillment of the 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria supported the diagnosis of giant cell arteritis, despite a negative temporal artery biopsy. Management included high-dose glucocorticoids and delayed tocilizumab initiation due to the need for multiple vascular surgeries. Vision loss was irreversible, but systemic symptoms resolved and vascular interventions were successful. This case highlights the diagnostic and management complexities of biopsy-negative giant cell arteritis in patients with severe atherosclerotic vascular disease, emphasizing the importance of clinical judgment and established classification criteria when imaging and biopsy results are inconclusive.

Background: Adolescent girls living in disaster-prone coastal regions of Bangladesh face heightened nutritional vulnerability due to limited food access, poor dietary diversity, and environmental stressors. Despite growing concerns about adolescent malnutrition, few studies have examined the combined influence of food security, dietary diversity, and age on nutritional outcomes in these settings. Objectives: This study aimed to assess the association between dietary diversity, food security, and age with the nutritional status of adolescent girls in coastal Bangladesh. Methods: A cross-sectional survey was conducted among 345 adolescent girls aged 10–19 in Chattogram and Cox’s Bazar. Data on dietary intake were collected using a 24-hour dietary recall and a food frequency questionnaire. Household food security was assessed using a validated scale. Nutritional status was determined using BMI-for-age classifications. Bivariate and multivariate analyses explored associations between dietary diversity, food security, age, and nutritional status. Results: Among participants, 10.14% were underweight, and 29.85% were either overweight or obese. While 17.39% demonstrated high dietary diversity (≥7 food groups), the majority had moderate diversity (5 or 6 food groups) (59.42%). Food-insecure households were significantly more likely to have overweight or obese adolescents (p < 0.05). Although dietary diversity was associated with BMI in bivariate analysis, it was not a significant predictor in the multivariate model. Age showed a significant relationship with both dietary diversity and nutritional status. Conclusion: The findings emphasize the importance of addressing household food security and age-related nutritional vulnerabilities in coastal areas. Interventions should prioritize age-sensitive, culturally appropriate strategies to improve dietary quality and prevent the double burden of malnutrition among adolescent girls.

The Mambaka watershed is extends between latitudes 1 3°45'E and 14°15'E and longitudes 7°16'N and 6°45'N. The geology, various tectonic and structural events that have affected the Adamawa Plateau in Cameroon make it rich in multi-substance mining. The objective of this study is to map rare earth (REE) geochemical anomalies in the sediments of the watershed streams west of Mambaka, and to trace their origins and geochemical processes. Predictive maps from inverse distance interpolations (IDW), factor analysis (F1) or principal component analysis (PCA) and hierarchical bottom-up classification maps provided a better understanding of the central tendency, distribution and dispersion of REE in the samples and in the study area, based on standard deviation and variance values that generated two factors F1 (Ho-Tm-Er-Yb-Lu-Dy-Tb-Gd-Eu-Sm) and F2 (Pr-Nd-Ce-La-Sm) representing 92.44% of the total cumulative variance. The ratios Ce/Ce* > 0.78 and Eu/Eu* > 1 demonstrate positive anomalies in Ce and Eu, and clear differentiation. The normalized concentrations used to calculate fractionation ratios show that the values for LaN/YbN (0.58 to 1.34), LaN/SmN (0.61 to 0.88) and LaN/LuN (0.62 to 1.43) suggest higher fractionation in SS09 and lower fractionation in SS01. Similarly, the ratios La/Lu (61.71 to 143.46), La/Yb (9.00 to 20.72), La/Sm (4.02 to 5.83) and La/ Lu (61.71 to 143.46) confirm these higher ratios in SS09 and lower in SS01. The REE in the study area comes from hydrothermal processes based on high lineament densities at sampling points in igneous rocks with a mean ∑REE value of between 174-219 ppm.

With an increasing diversity of learners in today’s educational set-ups, there is an insurmountable need to cater for individual differences including the cultural variations among learners. It is therefore necessary for educators to develop culturally responsive teaching that enhances intercultural competencies of learners. As educators strive to provide inclusive learning environments in which learners from diverse cultural backgrounds learn equitably, differentiated instruction becomes a practical tool. This paper explores how differentiated instruction can support and enhance culturally responsive teaching by examining how tailored instructional approaches can bridge cultural gaps and enhance educational outcomes. The aim is to provide a comprehensive understanding of how educators can effectively integrate differentiated instructional methodologies to achieve the goals of Culturally Responsive Teaching. The study used a descriptive survey design to determine the use of differentiated instruction by junior school teachers in Kenya and a systematic review of literature, practical examples, and studies on teachers’ practices in culturally responsive teaching. The study outcomes indicated that teachers used various differentiated instructional strategies with flexible grouping being the most commonly used strategy. However, there arises a concern, that teachers were not very familiar with cultural variations of learners in their classrooms even as they developed their differentiated instructional strategies. Literature provided the principles and practices of culturally responsive teaching. The combination of these results were used to formulate a conceptual framework for Culturally Responsive Differentiated Instruction (CRDI) that provides insights for practitioners to develop and implement culturally responsive differentiated instructional strategies. The study recommends that a framework to support teachers in the implementation of inclusive and equitable curriculum through CRDI be developed, CRDI be integrated into the teaching processes and the teachers be trained on providing for learner differences through CRDI.

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

This paper examines the impact of primary level classroom environments on the acquisition of English as a second language (L2) in Bangladesh, comparing English-medium and Bangla-medium schools. The study investigates how different instructional approaches and early exposure to English influence language proficiency among students. Through a mixed-methods approach, including surveys, interviews, and proficiency tests, the research reveals significant differences in language acquisition outcomes between the two educational settings. Findings indicate that students in English-medium schools, who are exposed to Natural approach methods of language learning and immersive English-speaking environments, demonstrate higher proficiency in speaking and listening skills compared to their Bangla-medium counterparts, who primarily receive grammar-focused instruction. The study highlights the critical role of early exposure to English, with students who begin learning the language at a younger age showing better phonological and syntactic development. Additionally, the integration of technology in language teaching emerges as a valuable tool for enhancing language learning, particularly in contexts with limited classroom exposure. The research suggests that Bangla-medium schools could benefit from adopting more interactive, student-centered teaching methods and integrating digital tools to support practical language use. The study's findings have significant implications for educational policy, advocating for a shift towards more immersive and communicative teaching practices to improve English language acquisition in Bangladesh. This research contributes to the broader understanding of SLA and offers practical recommendations for enhancing language education in similar contexts.

Intestinal malrotation is an embryological abnormality modifying the classic anatomy of the small and large bowels, particularly the topographical one. Inflammatory bowel disease (IBD) is characterised by anatomical lesions with preferential intestinal tropism. These two conditions are rarely associated. They represent a real diagnostic and therapeutic challenge due to the overlap of non-specific symptoms and the complexity of the clinical management. We report the case of a young woman presenting with a flare-up of her IBD and a partial common mesentery syndrome. Clinicians should be aware of the potential pitfalls of the two entities to propose the adequate therapeutic strategy regarding the full understanding of the anatomy, notably when surgery is needed.

This paper investigates the constructability of angles multiples of 3 within the framework of Euclidean geometry. It makes a significant contribution by presenting the first geometric construction for all such angles, offering a rigorous solution to a longstanding geometric problem. The paper reaffirms the efficacy of Euclidean geometry in providing precise constructions and robust proofs for these angles, demonstrating the enduring strength of Euclidean principles from classical to modern times. The presented workflow goes beyond Euclidean geometry to examine non-Euclidean methods, particularly analytical approaches, revealing misconceptions that compromise the genetic and geometric rigor of Euclidean principles. The paper exposes incongruities when algebraic proofs related to angle constructability are applied to the Euclidean system, emphasizing the misalignment of fundamental geometric concepts. A notable result in the paper is the construction of a angle, introducing the “ angle chord” as a novel geometric property. This property challenges assumptions made by non-Euclidean methods and highlights the nuanced geometric properties crucial for rigorous constructions. The paper refutes the fallacy of relying solely on algebra for solutions to angles multiples of , emphasizing the necessity of embracing Euclidean geometry for geometric discoveries. The paper underscores the merits and resilience of Euclidean geometry, showcasing its independence and depth across historical and modern perspectives. The newly presented geometric construction not only resolves a longstanding question but also emphasizes the intrinsic strength and uniqueness of Euclidean principles in contrast to alternative methodologies.

Biodiversity is related to a global problem: its destruction, a fact supported by scientific authorities. It is not trivial that educational dimension has been contemplated as one of the strategies for its conservation. Since 1992 global initiatives such as the Convention on Biological Diversity postulates concepts that linked education and nature conservation. The main objective of this research work is to test the level of assimilation of extracurricular scientific knowledge by primary school pupils. The method chosen for the content was, on the one hand, a master class intervention with an interactive presentation on a digital whiteboard. Third cases were chosen. Each case consisted of a presentation of the current status of a species of fauna present in Spain. On the second part, students were asked to write an essay and to illustrate the experience during the presentation. Regarding the essays, students showed that they were more attracted to the first species that was presented (Iberian lynx) in a proportion of over 45% of cases. The “Endangered species” concept appeared in more than 77% of the texts reviewed. In terms of drawings, almost 55% of the responses seem to devote more attention to the second species described (Testudo graeca). An attempt was made to offer rigorous, structured information related to different aspects of natural reality in order to contemplate the broadest possible vision. The example of an instructional intervention presented here aims to be an alternative to other transmissive teaching models. Likewise, the linking of abstract concepts with socio-cultural reality proved to be a successful strategy to reinforce knowledge about natural biodiversity, endangered species or threat factors.

Major depressive disorder (MDD) is a significant psychiatric condition, with many affected individuals not gaining remission from conventional treatments, leading to classification as treatment-resistant depression (TRD). This study aimed to investigate the potential of intravenous (IV) ketamine, particularly the S-enantiomer esketamine in nasal spray form, for treating patients with TRD and associated comorbidities. We report three cases of patients with diverse psychiatric and medical backgrounds whom all reported significant symptomatic relief from depressive episodes and suicidal ideation (SI) following esketamine administration. Additionally, esketamine seemed to proffer benefits beyond the primary depressive symptoms, positively impacting other comorbid conditions, such as agitation, self-injurious behavior (SIB), and chronic pain. The goal of this paper is to highlight that while esketamine's primary utility is in addressing TRD, its therapeutic potential may extend to a variety of associated conditions. However, it is crucial to underscore the heterogeneity of MDD, emphasizing the necessity for individualized therapeutic approaches and further research into esketamine's broader applications.

With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.

Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to over fit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered ( the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non – parametric regression, non – parametric forecasting, spatial, survival, and econometrics observations.

The paper considers a specific type of such financial instrument as an option, namely an exotic barrier call option of the European type. Exotic options are gaining popularity among ordinary investors due to the development of information and telecommunication technologies, thanks to which such specific financial instruments as options have become readily available. We investigate the hedging problem for such options with some restrictions on the payment function and the availability of dividend payment on a risky asset in the classical Black-Scholes model. An analogue of the Black-Scholes formula for the mentioned variant of the exotic barrier is proved. In the future, it is planned to generalize the obtained results for put options and for more general payment functions.

Poor irrigation water quality due to oil spillage on surface water can result in food insecurity, health and economic challenges. This paper investigated the effect of total petroleum hydrocarbon (TPHC) and lead (Pb) on irrigation water quality in the oil spill prone area of the Niger Delta region of Nigeria. Water samples were taken from five different sections labelled A, B, C, D, and E along the Choba River, in Rivers State, Nigeria. Sections B, C, D and E were direct industrial effluent discharge points while section A was without direct industrial effluent discharge. Standard methods were employed in the water sampling and analysis. Suitability of Choba river water for irrigation was assessed by comprehensive pollution index (CPI) that incorporated salinity, sodicity, and permeability hazard potentials as well as the specific toxicity hazard potentials of TPHC and Pb. Results showed that all primary water parameters except pH were within the Food and Agriculture Organization (FAO) guidelines. The pH was low, ranging between 4.48 and 5.6. TPHC values for four out of the five samples were greater than the 10mg/l guideline as recommended by the Directorate of Petroleum Resources for surface water. TPHC for the four samples ranged between 14.52 and 174.32mg/l. The parameters with the most impact on CPI include EC, PI and TPHC with TPHC having the most impact. Water samples from sections A, B and E with CPI values 0.14, 0.37, and 0.8 respectively were classified in the clean, sub clean and slightly polluted categories respectively, while water samples from sections C and D with CPI values greater than 1 range from moderately to heavily polluted and not suitable for agricultural irrigation. Only water sample A was found suitable for irrigation.

Student engagement in educational activities is essential for achieving desired learning outcomes. Despite this, little is known about the engagement patterns of nursing students from diverse or disadvantaged backgrounds. A mixed method study was conducted to explore engagement patterns within and outside the classroom but not during clinical placements. Students were asked what engagement means to them and what influences their engagement. Students were also asked how many hours they engaged in each of their undergraduate subjects and the reasons for this. The study was conducted at an Australian education provider. All students (n = 240) enrolled in the Bachelor of Nursing course were invited to participate. Lecture attendance was high at the start of the semester, fluctuated weekly and declined as the semester progressed. Students averaged between 3.5 and 4.4 hours of engagement per subject per week. They defined engagement as actually coming to class and a commitment to learning. Students were engaged by new, interesting content and disengaged by repetitive or complex content and poor tutoring. Most students want to engage but are distracted by intrinsic and extrinsic factors. Research should explore how to best assess students without the concurrent academic workload interfering with their studies.

Studies have highlighted the benefits of frequent quizzing in class. Frequent quizzing can promote more student attendance, engagement, practice and review, and achievement. Conversely, the opponents of frequent quizzing suggest that too frequent testing might hinder learning by frustrating anxious students and inhibiting larger units of instructional material. Notably, most studies have used degree examinations to evaluate the impact of quizzes on student learning and performance, yet little is known about whether quizzes can reinforce student performance in the in-course assessments (ICAs) despite ICA importance in student learning. The present study aimed to test the hypothesis that administration of weekly MCQ quizzes can enhance the leaning outcomes and performance of biomedical science students in assessment methods such as essay and oral presentation that can directly measure and provide information about student learning. It was therefore limited to in-course assessments. We found that the performance of the weekly quiz student group is remarkably better than that of the control student group in both the essay and oral presentation ICAs, which are two measures and indicators of student learning, suggesting improved student learning outcomes and performance after administrating weekly MCQ quizzes that also promoted student attendance in classrooms. The findings of this research study have implications for students, teachers, and curriculum designers in higher education.

In this paper we present a new classes of solutions for the Einstein-Maxwell system of field equations in a spherically symmetric spacetime under the influence of an electric field considering the MIT bag model equation of state with a particular form the metric potential that depends on an adjustable parameter. The obtained solutions can be written in terms of elementary functions, namely polynomials and algebraic functions. The obtained models satisfy all physical properties expected in a realistic star. The results of this research can be useful in the development and description of new models of compact structures.

Although there are numerous studies on the floristic composition of the Andean and Caribbean grasslands, there are limited information from a phytosociological point of view. This work therefore aims to highlight the differences in this important vegetation type in these two biodiversity hotspots to gain a better understanding of their floristic and vegetational richness. The floristic composition of Andean grasslands (Ecuador) and the island of Hispaniola is also studied. The reason for this comparative analysis of Andean and Caribbean territories is their tropical character and their bioclimatic similarities. The comparative analysis of Peruvian Andean and Colombian grasslands is based on floristic knowledge from the literature, our inventories on the island of Hispaniola, and the works of Peguero. We applied the phytosociological methodology of Braun-Blanquet and the code of phytosociological nomenclature. We performed a statistical analysis to verify the differences between both community types using the statistical packages PAST© and CAP3. The comparative analysis of Andean and Caribbean grasslands shows a clear floristic and biogeographical differentiation and reveals that the only ecological character they share is their high-mountain attribute. In the Caribbean these communities are found only on the island of Hispaniola (Dominican Republic) in the Central biogeographic sector, Central subprovince, Hispaniola province. These grasslands are exclusive to Hispaniola and have a high rate of endemics. We propose the following new syntaxa: the association Hyperico constanzae-Danthonietum domingensis, the alliance Hieracio domingensis-Deschampsion domingensis, the order Danthonietalia domingensis, and the class Deschampsio-Danthonietea domingensis. We found high biodiversity values for Andean and Caribbean areas. This is due to the intense anthropic activity on the island of Hispaniola, which has led to a high percentage of naturalized plants.