Growing demand for National Forests (NFs) recreational activities makes it crucial to understand the attitudes towards valuing public recreational resources and the potential conflicts with other functions of the forests. The study was conducted to identify the primary drivers influencing individual participation in outdoor recreation on NF lands in the southeastern region of the US among participants of various socioeconomic backgrounds. The study was based on the 2010–2014 dataset of fourteen NFs across thirteen states in the Southeastern USA—retrieved from the United States Department of Agriculture (USDA). Different statistical models and statistical analyses were utilized for the study. The statistical results revealed that individual needs for relaxation were the main driver for participation in forest recreation for the whole sample and pulled data (approximately 52% of the participants). It has been noted that the drivers varied depending on the forest. The personal need for mental development was the least valued driver with only 2%. Some significant differences were observed by gender, age category, and income level. The study results have practical importance for different stakeholders such as tourism operators, the USDA Forest Service, and local authorities.

Cancer remains a major global health challenge, characterized by abnormal cell growth and metastasis. Current limitations of conventional therapies, particularly non-specific toxicity harming healthy cells, highlight the need for more targeted approaches. Nanotechnology offers a revolutionary solution, utilizing nanoparticles (NPs) for precise drug delivery to tumor sites while minimizing off-target effects. These nanometer-scale particles enable superior binding to cancer cell membranes, the tumor microenvironment, or nuclear receptors, facilitating significantly higher local concentrations of therapeutic agents. NPs, synthesized via physical, chemical, or biological methods, are categorized as organic (organic material-based) or inorganic (metallic particle-based). Key delivery mechanisms include the Enhanced Permeability and Retention (EPR) effect and Active Transport and Retention (ATR). This review specifically examines NP applications for the most prevalent cancers in the US (2025): breast, prostate, and lung. Gold and magnetic NPs show significant promise for early breast cancer detection. For lung cancer, polymeric NPs like PCL, PLA, and PLGA are effective carriers for peptides, proteins, and nucleic acids. BIND-014, a docetaxel-loaded NP formulation, represents an emerging strategy for prostate cancer. Clinically established examples include liposomal doxorubicin and albumin-bound paclitaxel. We comprehensively discuss the synthesis methods, delivery mechanisms, and the current landscape of NPs in research and clinical trials for these cancers. This analysis underscores the potential of nanotechnology to provide more effective and targeted therapeutic options for cancer patients in the future. A distinctive feature of this review is its comparative cancer-specific analysis of NP platforms in breast, prostate, and lung cancers. Unlike previous generalized reviews, this work integrates synthesis strategies, delivery mechanisms, translational challenges, and clinically relevant formulations to provide a bench-to-bedside perspective on the future of nanomedicine in oncology.

Previous studies on global food security have indicated that in order to sustain the global population by the year 2050, a significant increase in food production will be needed. Consequently, it is crucial that today’s students are educated to realize this increasing food demand. One of the problems is that currently too few students seem to be interested in pursuing studies in the fields of agriculture and related sciences. By exploring three research questions, this study assesses the extent to which high school students in each of the 67 counties in the U.S. state of Alabama are being exposed to agricultural-related science courses. For the purposes of this assessment, the high schools were grouped by zones—Northern, Central, Southern and the Black Belt. Relevant high school data, including courses offered, were compiled from Alabama’s Department of Education 2017 directory. Microsoft Excel and SPSS software were used to analyze the data. The findings of this study demonstrated that there are spatial differences in agriculture-related courses offered in high schools in Alabama’s Northern, Central and Southern regions. Future research should further investigate what percentage of high school students actually choose agriculture or related disciplines as their professional career paths after graduating from high school.

Outdoor recreation is one of the most widely recognized ecosystem services provided by forests and grasslands in the world. This paper examined the influence of factors not related to landscape values, such as ethnicity and gasoline prices, on individual participation in outdoor recreation in the southeastern region of the U.S. The model results showed that there were no significant (p > 0.05) differences between the race groups (Caucasians and non-Caucasians) for participation in the different activities either between racial groups or among National Forest (NF). This may be due to the very high proportion of Caucasian participants in the study. The results also revealed that travel costs negatively influence the number of NF visits. The number of NF visits decreases if the gasoline price increases by 20% and more. The results of this study have practical importance for different entities such as stakeholders, tourism operators, the United States Department of Agriculture (USDA) Forest Service, and local authorities.

Coffee production in Peru plays a crucial socio-economic role, supporting over 200,000 families and contributing significantly to export income. However, the spatial variation in coffee farming across ecological and socio-economic regions remains poorly understood. This study examines spatial patterns of coffee farm clustering in three Peruvian mountainous regions (Moyobamba, Tingo María, and Tocache) using descriptive statistics, geospatial visualization, and unsupervised clustering techniques. Farm-level reports and government geospatial records covering 2019–2023 were analyzed to evaluate cultivation area, altitude, and spatial distribution. Kernel density mapping, Moran’s I spatial autocorrelation, and Local Indicators of Spatial Association (LISA) were applied to identify statistically significant clustering patterns, while regression analysis and DBSCAN clustering were used to evaluate spatial trends and production hotspots. Moran’s I indicated moderate spatial clustering (0.34, p < 0.001), while regression analysis showed a weak negative association between altitude and cultivation area (β = −1.144 × 10⁻⁴, adjusted R² = 0.023). Results suggest that measured environmental variables explain only a limited proportion of spatial variation in coffee production, indicating that additional unmeasured factors, potentially including socio-economic influences, may contribute to observed clustering patterns. These findings highlight the value of spatial analysis for understanding production heterogeneity and for supporting regionally adapted agricultural planning strategies.

This study introduces Meta-FedGeo, a federated learning framework that integrates meta-learning and spatiotemporal transformers to address key challenges in urban GeoAI for smart cities. Data streams in smart city environments are inherently non-stationary and heterogeneous, limiting the adaptability of traditional federated learning approaches. Meta-FedGeo overcomes these limitations through a hybrid centralised–decentralised architecture that pre-trains a global model using meta-learning to capture cross-city spatiotemporal patterns and dynamically refines it through federated updates. The framework incorporates performance-aware client selection and temporally weighted aggregation to enhance model robustness and convergence. To model complex urban dynamics, the proposed system employs a Spatio-Temporal Transformer (ST-Transformer). In addition, an Uncertainty-Calibrated Decision Engine (UCDE) is introduced to align model predictions with accessibility and urban planning constraints. Unlike static federated methods, Meta-FedGeo can dynamically identify and filter malicious or low-quality clients using local validation loss, while Shapley value-based mechanisms support efficient and fair knowledge transfer across distributed nodes. To clarify the scope of the present study, Meta-FedGeo is reported as a partially implemented research prototype: the ST-Transformer backbone, the meta-learning initialisation, the validation-loss-based client filtering and the temporal-weighted aggregation were implemented and evaluated on partitioned real-world datasets, whereas the Shapley-value contribution assessment, the Lightweight Data Harmonisers (LDHs) and the UCDE are presented as architectural components with proof-of-concept implementations whose full empirical validation is identified as future work. The framework is designed for seamless integration with existing urban infrastructure without requiring major modifications. Experimental results using real-world urban datasets partitioned into non-IID federated clients indicate improved predictive performance and faster convergence relative to the federated baselines considered here. Overall, Meta-FedGeo advances GeoAI toward scalable, adaptive, and practical applications in smart city environments.