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.

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.

Extracurricular activities and academic performance are connected in every aspect of the education system. Daffodil International University is one of the top universities in Bangladesh that focuses on student improvement through extracurricular activities. Extracurricular activities help students improve skills like leadership, teamwork, and analytical abilities. Do extracurricular activities help English as a Foreign Language (EFL) students improve their academic performance? This evaluation aims to find out this question among Daffodil International University students. The study focused on both qualitative and quantitative data. Therefore, the data analysis followed a mixed method. The quantitative data focused on the students' participation in extracurricular activities. Respectively, the comparison between their participation and EFL course improvement. On the other hand, the qualitative data focused on the interviewee's experience. However, it's been proven that though extracurricular activities help students improve their other soft skills, they actually don't have as much impact on improving their EFL course curriculum performance.

A persistent problem in the pharmaceutical industry that has existed for centuries is the prevalence of counterfeit drugs, and the World Health Organization (WHO) estimates that millions of people are affected by this issue each year. In particular, 4 out of 10 drugs in poor or underdeveloped countries may be adulterated, which can lead to severe adverse events. To address this problem, many countries have implemented regulatory compliance measures such as pharmaceutical drug serialization, which requires the unique identification of every drug package manufactured. This enables tracking and tracing of individual packages throughout the supply chain and helps to prevent counterfeit drugs from entering the market. In this paper, we conduct a systematic review of the serialization process evaluation and its impact on the pharmaceutical industry. We discuss the benefits of pharmaceutical drug serialization, including its ability to improve drug security and reduce adverse events and investigations. We also examine the challenges associated with implementing serialization processes and the regulatory requirements necessary for compliance. Finally, we explore the various tracking and tracing technologies used in serialization processes and their effectiveness in preventing the distribution of counterfeit drugs. Overall, this paper highlights the importance of pharmaceutical drug serialization in ensuring the safety and efficacy of drugs in the healthcare industry, particularly in poor or underdeveloped countries where the problem of adulterated drugs is especially prevalent.

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.

The government of Mexico City carried out the introduction of palm trees in the 50s to increase the visual appearance of the main avenues. As a result of introduction of these exotic species, phytosanitary problems appeared. Currently there is scarce information on how to evaluate the health of these majestic monocots, in this sense, it is necessary to implement assessment scales to determine the health condition of the most frequently found species to advance on their care and management. The present study had the following objectives: 1) To determine the current state of health of palm trees by means of a scale of visual evaluation of the crown; 2) To know the diversity and structural characteristics of palm trees and 3) To determine the influence of composite variables on the ecosystem services such as the amount of shade provided. Health of palm trees were evaluated two times (dry and rainy seasons in 2022) on 35 transects of 200 m length. An imaginary circle divided into twelfths was overlapped on palm tree “crowns”, and through it, two absolute variables, Live Crown Ratio (LCR) and Crown Quality (CQ) were evaluated. Composite variables were also calculated. The 12/12 health scale adapted in the present study was useful. Four health categories were obtained for the Live crown ratio (LCR): 7.62% of the palm trees were in critical condition, 7.80% were in intermediate condition, 80.36% were in normal condition, and 4.20% were in excellent condition. Meanwhile, for crown quality (CQ), the percentages were 13.50%, 20.00%, 56.96% and 0.43%, respectively. The total height and “crown” diameter showed a positive correlation with the volume composite variables. The shadow area projected as an important ecosystem service increased as the health of the palm trees improved. This is the first study on palm trees health assessment in Mexico City.

Risks in Public Private Procurement mass housing project (PPP-MHP) initiatives are emerging and this requires early risk identification and allocation to achieve the goal and sustenance of the scheme. The study, being a follow-up of a Delphi survey, elicits the opinion of respondents on the probability of occurrence and severity of identified risks in PPP-MHPs in Nigeria. The study adopts a quantitative research design approach by administering structure questionnaire survey on identified PPP-MHPs partners in Abuja, Nigeria. Data analysis was performed using descriptive and inferential statistical tools such as Mean item score (MIS), standard deviation, and Kruskal Wallis analytical techniques with the aid of SPSS software packages. The findings show that all the listed risk factors were found to be extremely high, very high, high, or moderate in terms of occurrence while all the listed risk factors recorded a very high level of severity on the delivery of PPP-MHPs. The top ten (10) risk factors frequently associated with PPP-MHPs are non-availability of finance, high finance cost, non-involvement of the host community, poor execution of housing policies, corruption and lack of respect for law, wrong perception of housing need by low-income earners, Illegal title to land, land acquisition and site availability, level of demand for the mass housing projects and unstable value of local currency. The respondents differs significantly on 29 risk factors in terms of occurrence and 40 risk factors in term of severity. The study, therefore, recommends that risk management culture should be highly encouraged among the PPP Partners in the sector. The study intends to enumerate the rate of occurrence of some itemized risk factors and their severities on the delivery of PPP – procured mass housing projects in Nigeria and the need to bookmark these risk factors in ensuring the sustainability of the PPP mass housing scheme.

The study accessed the critical risk factors in public-private partnership (PPP)-procured mass housing project (MHP) delivery in Nigeria. The research design adopts a quantitative approach, using well-structured questionnaires distributed to stakeholders involved in PPP-MHPs i.e. consultants, in-house professionals, contractors, and the organized private sector (OPS) registered with PPP departments in the Federal Capital Territory Development Authority (FCDA) Abuja, Nigeria. The instrument relates to the background information of respondents and the risk peculiar to PPP-MHP. Sixty-three (63) risk factors were submitted for the respondents to rank using Mean Item score (MIS) for risk occurrence and its severity, while risk significance index (RI) was used to determine the risk impact. Fuzzy Synthetic Evaluation (FSE) method was subsequently applied to determine the risk criticality groups and the overall risk level in the sector. The fuzzy set theory deals with ambiguous, subjective and imprecise judgments peculiar to decision making in construction project risk assessment. It aims to provide a synthetic evaluation of an object relative to a fuzzy decision environment with multiple criteria that requires qualitative linguistic terms. The findings show that thirty-one (31) risk factors were critical in the sector while financial and micro-economic risk group is contributing most significantly to the overall risk level in PPP-MHPs in Nigeria. The top 10 risk factors in the sector include availability of finance, high finance cost, the unstable value of the local currency, lack of creditworthiness, influential economic events (boom/recession), high bidding cost, poor financial market, financial attraction to project investors, interest rate volatility, inflation rate volatility, corruption and lack of respect for the law, non-involvement of the host community and poor execution of housing policies. The implication for practice is that having known the risk group contributing most significantly to the overall risk level in PPP-MHPs, adequate financial and budgetary allocation should be made available before embarking on such venture so as to sustain the scheme in the country. The study is one of the recent researches conducted on housing, since the procurement option is novel in the sector. The study is of immense value to PPP actors in providing necessary information required to formulate risk response methods in minimize the identified risk impact sector.