Despite a rapid rise of AI-powered Unmanned Aerial Vehicle (UAV) deployments in smart city environments, current surveys and frameworks lack a unified, protocol-level reference architecture that integrates multi-domain applications, edge AI perception, cognitive reasoning through Large Language Models (LLMs), and regulatory compliance within a single deployable specification. This study presents a comprehensive cross-domain review of AI-powered drone systems for traffic management, delivery, infrastructure inspection, disaster response, and environmental monitoring. The study introduces COMPASS (Cognitive Operations Model for Programmable Autonomous Smart-city Systems), a novel seven-layer technical reference architecture that describes communication protocols (MAVLink 2.0, ROS2/DDS, MQTT 5.0, and NGSI-LD), edge computing hardware recommendations for five drone payload tiers, and quantified performance requirements for safety-critical operations. The key feature of COMPASS is its LLM-based Semantic Middleware Layer, which allows for context-aware decision-making, natural human-drone interaction, and regulatory compliance verification. Comparing COMPASS to many other frameworks reveals that it is the only architecture to simultaneously provide multi-domain coverage, protocol-level specifications, hardware recommendations, LLM integration, and empirically verified benchmarks.
Reaction-bonded silicon carbide (RBSC) ceramics prepared by gel casting and two-step sintering were investigated. Three active carbon sources of petroleum coke (PC), carbon microspheres (MC), and nano-carbon black (CB) were compared in terms of slurry rheology, preform characteristics, sintered microstructure, and mechanical properties. With the active powders of PC and MC, the large particle size resulted in low density of the preform and un-uniform distribution of active carbon. CB addition yielded the highest slurry viscosity, the highest preform density, and the highest carbon density of 1.00 g·cm−3. The higher carbon density and more uniform active carbon translated into the highest SiC phase content and the lowest residual Si after sintering, attributed to the uniform active carbon distribution. A high-performance RBSC ceramic with a density of 3.12 g·cm−3, bending strength of 512 MPa, and Vickers hardness of 2386.6 HV was achieved. The corresponding phase composition was 94.28 vol.% SiC, only 2.22 vol.% residual Si, which is significantly lower than that of conventional RBSC. These results highlight the critical role of active carbon source selection in optimizing RBSC performance through microstructural refinement and residual phase control.
The present study aimed to examines regional inequalities in age at first marriage among Bengali-speaking women in Howrah district, West Bengal, It hypothesized that women in urban areas were more likely to marry after 18 years compared to rural women. The analysis draws on cross-sectional data collected from 665 ever-married women, of whom 60.15% resided in urban areas and 39.85% in rural areas. Bivariate analysis, independent sample t-tests, and binary logistic regression were employed, complemented by qualitative in-depth interviews from each region. The mean age at marriage was 22.25 years (±4.4), with a pronounced rural–urban regional difference: rural women married significantly earlier (19.83 years) than urban women (23.85 years) (t = 12.80; p < 0.001). Nearly 48.30% of rural women were married at or below 18 years, compared to only 7.25% of urban women (p < 0.001). Logistic regression results reveal strong and persistent regional disparities. In the unadjusted Model I, urban women had significantly higher odds of marrying after 18 years than rural women (OR = 11.95; p < 0.001). After adjusting for socio-demographic, familial, and economic factors in Model II, the association remained robust (OR = 9.67; p < 0.001). Generational patterns were non linear: women from Generation II were more likely to marry after 18 years (OR = 1.09; p < 0.01), while those from Generation III had significantly lower odds (OR = 0.39; p < 0.01). Higher education of respondents (OR = 1.66; p < 0.01), respondents’ fathers (OR = 3.12; p < 0.01), and mothers (OR = 3.58; p < 0.01) substantially increased the likelihood of delayed marriage. Respondents (OR = 1.51; p < 0.05) and respondents’ fathers (OR = 1.92; p < 0.05) with white-collar jobs significantly increase the likelihood of being delayed in marriage. Respondents belonging to the upper wealth quintile (OR = 1.92; p < 0.05) were more likely to marry at later ages. Respondents with ≥3 siblings(OR = 0.65; p < 0.05)and those whose husbands had 1–2 siblings (OR = 0.37; p < 0.01) and ≥3 siblings (OR = 0.39; p < 0.01) were significantly less likely to marry after 18 years compared to the reference category. The qualitative findings reveal the intersection of socio-cultural and kinship obligation in marital timing. The finding underscores that delaying marriage requires interventions beyond legal enforcement and schooling alone, highlighting the need for rural-specific, intergenerational, and economically grounded policy strategies.
Next-generation cancer immunotherapy increasingly recognizes the tumor microenvironment (TME) as a decisive regulator of therapeutic efficacy and durability. While immune checkpoint blockade and other immunotherapies have achieved remarkable clinical success, sustained benefit remains limited to a subset of patients, underscoring the insufficiency of immune activation alone. Accumulating evidence reveals that the TME functions as a dynamic immune ecosystem that shapes immune cell infiltration, metabolic fitness, spatial organization, and effector function. Static or reductionist biomarker frameworks fail to capture the temporal and functional heterogeneity of TME states that govern immunotherapy sensitivity and resistance. Importantly, immunotherapeutic interventions themselves induce adaptive TME remodelling, frequently triggering compensatory immunosuppressive circuits and acquired resistance. In this review, we synthesize recent advances in understanding functional and evolving TME states and discuss how strategic modulation of the microenvironment can enable more durable and context-dependent immunotherapy responses. By reframing immunotherapy as a process of TME state management rather than isolated immune stimulation, this perspective outlines guiding principles for designing adaptive, TME-driven immunotherapeutic strategies.
Attention-Deficit/Hyperactivity Disorder (ADHD) presents diagnostic challenges due to heterogeneity, comorbidity rates, and reliance on subjective, phenomenological criteria, resulting in misdiagnosis or treatment delays. This structured narrative review with quantitative tabular synthesis, conceptual mapping, and clinical workflow integration employed a sunflower life-cycle metaphor to bridge clinical expertise and machine learning (ML) technologies, while surveying recent empirical studies (2017–2023) to capture methodological variation in ADHD assessment workflows. Ten studies were selected based on relevance to ML applications for ADHD identification and classification, with deliberate representation of diversity in study design, sample characteristics, data modalities, and ML model-type. The method comprised (a) broad interpretive literature searches, (b) extraction of study-level data, and (c) mapping of ML approaches onto a standardized evidence-based ADHD assessment workflow. Analyses included qualitative synthesis of sample characteristics (youth-focused, N = 38–238,696), data modalities (behavioral surveys, EHR, neuroimaging, genetics), ML models (RF, SVM, DNN), performance metrics, phenotype- and genotype-based distinctions; quantitative aggregation of reported performance metrics (accuracy 66–93%, AUC 0.66–0.94); cross-validation practice, and model-level considerations; and tabular summarization of limitations and multidimensional predictors. Syntheses produced comparative tables, a human–AI diagnostic workflow diagram, and explicit alignment of ML applications with each clinical stage to highlight integration points and gaps.
