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.

Artificial Immune Systems (AIS) are bio-inspired computational frameworks that emulate the adaptive mechanisms of the human immune system, such as self/non-self discrimination, clonal selection, and immune memory. These systems have demonstrated significant potential in addressing complex challenges across optimization, anomaly detection, and adaptive system control. This paper provides a comprehensive exploration of AIS applications in domains such as cybersecurity, resource allocation, and autonomous systems, highlighting the growing importance of hybrid AIS models. Recent advancements, including integrations with machine learning, quantum computing, and bioinformatics, are discussed as solutions to scalability, high-dimensional data processing, and efficiency challenges. Core algorithms, such as the Negative Selection Algorithm (NSA) and Clonal Selection Algorithm (CSA), are examined, along with limitations in interpretability and compatibility with emerging AI paradigms. The paper concludes by proposing future research directions, emphasizing scalable hybrid frameworks, quantum-inspired approaches, and real-time adaptive systems, underscoring AIS's transformative potential across diverse computational fields.

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.

Background: Oncology nurse navigators (ONNs) are becoming even more vital as healthcare continues to develop into a more complicated, confusing maze for patients. When many specialists on the treatment team have divergent points of view due to the nature of their respective professions or other factors, the patient may experience feelings of confusion. In the end, this can cause delays in treatment, pose a threat to the established standard of care, and lead to a decrease in patient satisfaction. Aim: To enumerate various ways in which ONNs may help improve the quality of life of cancer patients. Design: An integrative review. Results: A total of 19 studies related to the topic are evaluated. Four main themes namely: provider of psychological support, facilitator of timely care, oncology nurse navigators perception of their role and patient’s perception of oncology nurse navigators and 3 sub themes which are: information giver, source of emotional support and coordinator, were identified to be the roles of the ONNs. The findings showed that oncology nurse navigators help reduce patients anxiety and distress, increase patient satisfaction, shorten the time from diagnosis to treatment, provide necessary information, support them emotionally and coordinate their care with the different members of the healthcare team and resources. Conclusion: The main function of the ONNs is to address any barrier that may hinder the patient’s cancer treatment, survivorship and even palliative care. ONNs make sure that each patient has individualized nursing care according to the patients and their families' needs. Implications for Practice: ONNs have the potential to significantly contribute not only to the quality of life of cancer patients but also to the process of achieving better service integration. The result of this integrative review contributes to the growth of the healthcare system by improving accessibility, fairness, efficiency, effectiveness, and the ability to maintain health services throughout time brought about by ONNs.