This review discusses global trends in stroke resulting in disability and death. As stroke outcomes and their significant impacts are unpredictable, improved predictors are needed. This study evaluates the effectiveness and efficiency of machine learning (ML) and deep learning (DL) techniques in predicting stroke risk in different contexts. A systematic review of existing studies and literature was conducted using the Advanced Publications for Systematic Reviews and Meta-analyses (PRISMA) guidelines, focusing on various ML and DL algorithms used for stroke risk prediction. A total of 31 articles met the final inclusion criteria. This review highlights significant advances in stroke prediction with ML and DL models that can handle complex datasets while achieving high prediction accuracy. However, issues related to external validation, standard definition, and transparency remain unresolved. It is recommended to emphasize the importance of features as they can provide insight into the different risk of stroke across countries. The study also shows that the random forest model is the best model for predicting stroke risk, secondary data produces the largest data, and India, including China, and Bangladesh are the countries with the most research on stroke risk. Machine learning and deep learning provide effective ways to predict stroke risk, improving personalized treatment strategies. Solving existing problems is important for their successful integration into treatment.
Noise pollution from road traffic poses a significant challenge in Mumbai , with growing concerns over its impact on human health and well-being. Efforts to mitigate this issue have driven research into improved noise barrier designs. Reflecting on the experience of a recent acoustic barrier design project, this study investigates common experimental errors encountered when working with scaled models in open field tests. The team tested three noise barrier designs in an open field, using amplifiers to simulate traffic noise in the range of 800–1200 kHz. Concrete panels for the models were created using 3D-printed templates. The tests revealed inconsistencies between the experimental results and initial expectations. All models reduced noise at measurement points across varying heights, with reductions ranging from 4 to 11 dB(A) at lower heights and 3 to 5 dB(A) at higher heights. However, the plotted data showed non-linear trends by height, complicating definitive conclusions. The study identifies critical lessons learned at each project stage, from conceptualization to prototyping and testing. These insights aim to improve future experiments involving scaled model prototypes, enhancing the effectiveness of noise barrier designs.
The United States, with over 340 million people, operates as a federal state with around 89,000 national, state, and local government units providing services. The federal government addresses nationwide needs, while the 50 states manage local services through their administrative structures. This study focuses on California, a state grappling intensely with homelessness, highlighting efforts by the “Institute of Local Governments” and initiatives under California law, particularly within the ‘Metropolitan Management Area.’ The research explores projects addressing homelessness, emphasizing needs like belonging, privacy, supportive environments, rehabilitation, and socialization. The distribution of funds and policies aimed at assisting the homeless are analysed, along with initiatives that foster reintegration into society. Nepal homelessness crisis underscores the global nature of this urban challenge, making its strategies critical for study and replication.
The main objective of this chapter explores the growth and economic feasibility of fish species cultured on local feeds in mid-altitude regions of Uganda against high altitude challenges with regard to ecological constraints. The main research question involved is the impact of the quality of the environment as well as local feeds in the growth and economic performances of Rainbow trout, Nile tilapia, and Mirror carp. There are five sub-research questions that guide this study, namely: the impact of environmental quality on the growth of Rainbow trout, influence of local feeds on the economic viability of Nile tilapia, growth comparison between Rainbow trout and Mirror carp, profitability analysis of Mirror carp, and feed quality role in fish performance. A quantitative methodology is used, and the discussion centres on independent variables and dependent variables: environmental quality and feed quality as independent, and growth rate and economic viability as dependents. The paper then moves from literature review to methodology, results, followed by a discussion on both theoretical and practical implications, focusing on the importance of optimized feeds and management practices.
This research explores the antimicrobial properties of Streblus asper (S. asper) against Streptococcus mutans (S. mutans), a primary contributor to dental caries. Traditional Thai medicine has recognized S. asper for oral health, prompting its evaluation as a natural oral care agent. Using a quantitative methodology, the study examines extracts from the plant's leaves, bark, and branches. Results demonstrate the leaf extract's superior inhibitory effect, significantly reducing bacterial survival rates to 25.55±1.26%, compared to bark and branch extracts with survival rates of 40.46±0.65% and 37.30±3.90%, respectively. Bioactive compounds such as flavonoids and tannins are identified as key contributors to antimicrobial activity, disrupting bacterial cellular processes. These findings highlight the potential of S. asper extracts in formulating natural oral care products like toothpaste and mouthwash. While the study establishes promising antibacterial efficacy, future research should focus on clinical trials, synergistic formulations, and long-term safety evaluations to ensure its viability as a sustainable alternative to synthetic antimicrobial agents.