Search Results (118)

This study provides evidence on the impact of a heavy rail-based suburban metro station in Fremont, California, on house prices using geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The dataset comprises sale price, sale date, and property and locational characteristics for single-family houses sold during the January 2000–April 2018 period within two miles of the Warm Springs Station on the San Francisco Bay Area Rapid Transit system. The results demonstrate that, relative to the referent period (2000–2001): (a) the station-led house price impacts are spatially heterogeneous—the house price increased in one pocket only, comprising less than one-tenth of the dataset, not across the entire 0–2 mile station area, (b) the price increase in that pocket began right after the project announcement period, and (c) the price impacts indicated by the MGWR model markedly differ from baseline ordinary least square regression (OLS) estimates. Overall, the study’s findings highlight the need to move beyond average estimation techniques, such as OLS, to those that account for spatially heterogeneous price impacts, such as GWR and MGWR. Full article

Accurate short-term vessel trajectory prediction is important for traffic monitoring and collision-risk screening in port-approach waters, where vessels frequently turn, accelerate, decelerate, and merge into traffic lanes. This study develops a maneuver-aware residual multi-scale long short-term memory (LSTM) framework for Automatic Identification System (AIS)-based 10–30 min trajectory prediction. The method predicts residual displacement relative to the last observed position, constructs maneuver-aware features from local displacement, course variation, speed variation, turning rate, and acceleration-like terms, and compares fixed and maneuver-guided multi-scale fusion strategies. Experiments are conducted on public AIS data from San Francisco Bay and adjacent approach waters using Maritime Mobile Service Identity (MMSI)-level train/validation/test splits and three random seeds. The largest observed gains come from residual prediction and maneuver-aware features. In the three-seed main evaluation, the fixed multi-scale LSTM (Fixed-MS-LSTM) provides the strongest 10 min accuracy, while the maneuver-guided multi-scale residual LSTM (MGMS-RLSTM) achieves lower average displacement error (ADE) at 20 and 30 min and learns distinct temporal-scale preferences across straight, turning, and speed-changing samples. Encounter-oriented closest point of approach (CPA) and time to closest point of approach (TCPA) evaluation further shows that the residual multi-scale models support more accurate CPA/TCPA-based high-risk screening under the evaluated benchmark. These findings indicate that maneuver-guided fusion can be characterized as a horizon-dependent scale-selection mechanism that complements the fixed multi-scale counterpart. Full article

Background: Seasonal influenza prevention in young adults is influenced by access, trust, and vaccine information exposure, but local evidence linking vaccination uptake with illness and economic burden is limited. Methods: We conducted a non-probability, cross-sectional electronic survey of adults aged 18–49 years who lived, worked, or studied in the San Francisco Bay Area during the 2025 to 2026 influenza season. Measures included vaccination uptake, influenza-like illness, recovery, functional and economic burden, vaccination sites, and vaccine information exposure. Multivariable logistic regression examined factors associated with vaccination uptake; Kaplan–Meier and Cox models examined time to recovery. Results: Of 554 responses, 463 were included. Vaccination uptake was 86.2% (n = 399; 95% confidence interval [CI], 82.7–89.2%), likely reflecting a health-engaged convenience sample. Influenza-like illness was reported by 38.4%; median recovery time was 5 days, median missed work or school was 2 days, and median direct out-of-pocket cost was US$20. Prior season vaccination (adjusted odds ratio [aOR], 2.24; 95% CI, 1.15–4.34) and greater trust in Centers for Disease Control and Prevention (CDC) or public health agencies (aOR, 1.46; 95% CI, 1.05–2.02) were associated with vaccination. Pharmacies were the second most common vaccination site and preferred future site. Conclusions: Influenza prevention for young adults may benefit from pharmacy-inclusive, multichannel access paired with trusted communication. Findings should be interpreted in light of non-probability recruitment and likely overrepresentation of health-engaged respondents. Full article

Background: Despite a decline in lung cancer in the U.S., lung cancer among never-smoking Asian American (AsA) women is rising, and subgroup aggregation obscures heterogeneity. We compared primary lung cancer prevalence across disaggregated AsA subgroups and examined factors associated with prevalence such as personal- and family-cancer histories versus Non-Hispanic Whites (NHWs). Methods: This cross-sectional study analyzed electronic health records of AsA women (≥18) in a large Northern California health system (2010–2022). Lung cancer cases were obtained from the hospital registry and categorized by smoking status and self-reported ethnicity. Adjusted prevalence ratios (aPRs) were estimated using targeted maximum likelihood estimation, accounting for sociodemographic, smoking, and clinical covariates. Results: Among 1,843,119 women, 8651 had primary lung cancer; 2429 were never-smokers. In AsA never-smokers, aPRs and 95% confidence intervals versus age-matched NHW were: Chinese (3.36, [3.20–3.53]), Filipino (2.68, [2.55–2.82]), Vietnamese (2.07, [1.96–2.18]), Japanese (1.99, [1.89–2.10]), Korean (1.90, [1.80–2.00]), and Other Asian (0.35, [0.33–0.37]). Personal cancer-history reflected an increase in prevalence among Korean patients (2.91, [2.76–3.06]) while family cancer-history demonstrated increased prevalence among Chinese patients (1.51, [1.42–1.60]). Among women with uterine cancer, Chinese patients had higher lung-cancer prevalence than NHW (1.91, [1.58–2.31]). Conclusions: Never-smoking disaggregated AsA women show heterogeneous lung cancer prevalence, with higher prevalence in Korean women with personal cancer-history and in Chinese women with family cancer-history compared with NHW, supporting history-informed and ethnic-specific lung cancer screenings. Full article

Polarimetric Synthetic Aperture Radar (PolSAR) has significant advantages for land-cover classification for its all-weather, day-and-night, and multi-polarization observation capability. Traditional methods often exhibit limited classification accuracy in regions with strong noise and complex textures. Although deep learning methods can improve classification performance, they usually suffer from high model complexity, while lightweight models often show insufficient spatial consistency. To address these issues, this study proposes a PolSAR land-cover classification framework that integrates a Lightweight Dynamic Sequential Axial Network (LiteDSANet) with a polarization feature-guided Dense Conditional Random Field (PFG-DenseCRF). LiteDSANet is employed to generate the initial class probability map, and PFG-DenseCRF optimizes the classification results by introducing polarimetric features. Experiments were conducted on AIRSAR L-band and RADARSAT-2 C-band datasets from the San Francisco Bay and Flevoland regions, covering agricultural, urban, and natural land-cover scenes. The results show that the proposed method improves classification accuracy by 2.14~15.36% compared with other methods, while achieving a favorable balance between accuracy and computational efficiency. These results demonstrate the effectiveness of the proposed method for PolSAR land-cover classification in different regional environments. Full article

Advanced Air Mobility (AAM) includes innovative aviation technologies and services that could alter how people and goods are transported. However, future AAM growth and potential regional integration are uncertain and influenced by a range of factors. In this paper, we report findings from expert interviews (n = 35) and a scenario planning workshop (n = 32 stakeholders), conducted between August 2024 and July 2025, to explore potential alternative futures for AAM at the San Francisco International Airport (SFO) and the greater San Francisco Bay Area. We applied a two-axis framework: regulatory environment (supportive vs. restrictive) and economic conditions (vibrant vs. stagnant). Building on this, we developed four plausible scenarios for the 2025 to 2030 and post-2030 time horizons. We apply the SPELT (social, political, economic, legal, technological) framework to assess cross-cutting drivers, tensions, and indicators across the four scenarios based on two timeframes, i.e., 2025 to 2030 and post-2030. Our analysis of the scenarios reveals that regulatory clarity and macroeconomic conditions are key influencers that define the pace and scale of AAM growth, while community impacts (e.g., noise), public acceptance, and infrastructure availability are constraints. These factors largely determine whether technical readiness can translate into scaled deployment. Cross-cutting themes across all of the scenarios consistently shape the outcomes: (1) equity and community acceptance strongly influence political feasibility; (2) SFO and other airports can serve dual roles as conveners and practical enablers but face risks of stranded assets; and (3) flexible, modular infrastructure and incremental investment strategies reduce uncertainty for SFO and other Bay Area airports and public agencies. Together, the findings suggest that while the future of AAM is uncertain, policy and planning responses can assist airports, local governments, and other public agencies in preparing for potential developments. Full article

Magnetic resonance imaging (MRI) is a critical clinical tool that requires precise and reliable interpretation for effective brain tumor diagnosis and timely treatment planning. Deep learning methods have advanced automated tumor classification greatly in the last few years, but many of the current methods are still challenged by a lack of interpretability, a lack of testing on region-focused data, and a lack of model robustness testing. Such limitations reduce clinical trust and limit the practice of automated diagnostic systems. To address these challenges, this study proposes an interpretable deep learning model for classifying brain tumors using the PMRAM dataset, which is a Bangladeshi brain MRI collection containing four categories: glioma, meningioma, pituitary tumor, and normal brain.. The proposed pipeline combines image preprocessing and feature enhancement methods, and then it trains a series of squeeze-and-excitation (SE)-enhanced convolutional neural networks such as VGG19, DenseNet201, MobileNetV3-Large, InceptionV3, and EfficientNetB3. The SE-enhanced EfficientNetB3 performed best, with 98.70% accuracy, 98.77% precision, 98.70% recall, and 98.70% F1-score. Cross-validation also demonstrated stable performance, with a mean accuracy of 96.89%. The model also exhibited efficient inference with low GPU memory consumption, enabling predictions in about 2–4 s per MRI image. Grad-CAM++ and saliency maps were used to improve the transparency of the results, and it was found that the network was concentrated on the clinically significant parts of the tumor, which affected the model predictions. Further robustness analysis and cross-dataset testing are additional evidence of the generalization possibility of the model. An online application was also implemented to allow real-time prediction and visual explanation of brain tumors. Overall, the proposed framework offers a precise, interpretable, and promising solution to automated brain tumor classification using MRI images. Full article

Over nearly two decades, ride-hailing has become a major component of urban travel, and its tendency to increase vehicle miles traveled (VMT) and worsen congestion is now well established. What remains poorly understood is why pooling, the most frequently proposed remedy, consistently falls short of theoretical expectations. With access to proprietary platform data still limited, high-fidelity simulation offers a promising path to untangle these dynamics. Here, we implement three pooling algorithms alongside a demand-following repositioning algorithm, within Berkeley Lab’s BEAM (Behavior, Energy, Autonomy, and Mobility), an open-source, agent-based regional transportation model. In a high ride-hailing adoption scenario for the San Francisco Bay Area, we find a counterintuitive result: the more stringently point-to-point pooling is promoted, the more detour burdens erode matching feasibility and reduce vehicle occupancy rather than increase it, thereby compounding rather than offsetting VMT and congestion impacts. Sensitivity analysis further identifies inflection points in pooling match rates and repositioning sensitivity beyond which deadheading and negative network feedbacks begin to dominate. These results show that pooled ride-hailing has a constrained ability to reduce network-wide impacts and that effective shared mobility requires treating pooling, repositioning, and fleet sizing as interdependent levers. Full article

Traditional macroscopic fundamental diagram (MFD)-based traffic perimeter metering control strategies rely on full knowledge of vehicle accumulation and inter-regional flow dynamics, assumptions that seldom hold in heterogeneous and highly variable real-world networks. Classical data-driven reinforcement learning methods face similar constraints, often converging slowly and exhibiting low sample efficiency when confronted with such complexities. Motivated by these limitations, this paper proposes a Parameterized Deep Q-Network perimeter control (P-DQNPC) scheme designed for multi-region urban road networks. The framework jointly optimizes discrete actions (regional routing choices) and continuous actions (signal-timing or flow-duration regulation) within a model-free learning structure. The approach is first trained and validated on synthetic MFD data to establish stable and interpretable policy behavior under controlled conditions. It is then transferred and further evaluated using real-world measurements from the Performance Measurement System—San Francisco Bay Area (PeMS-SF), a dataset collected from 18,954 loop detectors across the California State Highway System. PeMS-SF is selected due to its high spatial and temporal resolution, broad network coverage, and strong ability to capture realistic and diverse congestion patterns qualities that support both rigorous validation and generalization to other metropolitan regions. Experimental results show that P-DQNPC consistently outperforms state-of-the-art baselines, including deep deterministic policy gradient, deep Q-network, and No-Control schemes. The proposed method achieves superior regulation of regional accumulations and demonstrates enhanced robustness in large, heterogeneous, and uncertain urban traffic environments. Full article

Understanding the hierarchical patterns of building heights is essential for sustainable urban development and planning. This study presents a novel approach for detecting and comparing building height hierarchies in four major bay areas: the San Francisco Bay Area, the New York Bay Area in the United States, the Tokyo Bay Area in Japan, and the Guangdong-Hong Kong-Macau Greater Bay Area in China. Kernel density estimation was first used to create continuous spatial distributions of building heights, forming the basis for our analysis. The approach then uses the contour tree algorithm to abstract and visualize these hierarchies. A structural similarity index is proposed to compare the hierarchies by identifying the maximum common sub-contour tree across the different contour trees. The results reveal that all four bay areas exhibit a multi-core hierarchical structure, with the greater bay area exhibiting the most complex pattern. Quantitative comparison reveals that the building height hierarchies of the New York Bay Area and Tokyo Bay Area are most similar (similarity index = 0.74), while those of the San Francisco Bay Area and Greater Bay Area are the least similar (similarity index = 0.17). Our approach provides a practical tool for understanding building height hierarchies and can be readily applied to analyze diverse spatial patterns. Full article

There are few studies on the extent to which waterfowl select plant food compared with what is available in wetland ecosystems. We used a new dataset on the presence of seeds in the alimentary canal or feces to identify flowering plant species whose seeds are ingested by North American ducks or geese. These data are a proxy for dispersal interactions because an important fraction of ingested seeds survives gut passage and is dispersed by endozoochory. We compared the plant traits of species whose seeds were ingested with those of species on the U.S. Department of Agriculture National Wetland Plants List (NWPL). Using a global dataset on plant form and function and chi-squared tests, we compared four categorical traits (moisture requirements, growth form, plant height, and seed mass) between species whose seeds are ingested by North American ducks and geese with the NWPL. Our analyses identified significant differences between the trait distributions of plants whose seeds were ingested by waterfowl guilds and those of the NWPL. Geese and ducks (except whistling ducks) ingested more aquatic and semiaquatic plant species than expected from the NWPL. All guilds except sea ducks ingested more herbaceous graminoids and fewer shrubs or trees than expected. Diving ducks interacted with fewer of the taller plants (>5 m) than expected, but otherwise plant height distributions did not differ from those expected. All waterfowl guilds ingested more species of intermediate seed size (1–10 mg) and fewer species of the smallest (<0.1 mg) or largest (>100 mg) size categories than expected. These results help to explain the role of the long-distance dispersal of seeds by migratory waterfowl in plant biogeography and how plant distributions are likely to respond to global change. Full article

Large-scale traffic simulations at a microscopic level can mimic the physical reality in great detail so that innovative transport services can be evaluated. However, the simulation times of such scenarios is currently too long to be practical. (1) Background: With the availability of Graphical Processing Units (GPUs), is it possible to exploit parallel computing to reduce the simulation times of large microscopic simulations, such that they can run on normal PCs at reasonable runtimes?; (2) Methods: ParSim, a microsimulator with a monolithic microsimulation kernel, has been developed for CUDA-compatible GPUs, with the aim to efficiently parallelize the simulation processes; particular care has been taken regarding the memory usage and thread synchronization, and visualization software has been optionally added; (3) Results: The parallelized simulations have been performed by a GPU with an average performance, a 24 h microsimulation scenario for Bologna with 1 million trips was completed in 40 s. The average speeds and waiting times are similar to the results from an established microsimulator (SUMO), but the execution time is up to 5000 times faster with respect to SUMO; the 28 million trips of the 24 h San Francisco Bay Area scenario was completed in 26 min. With cutting-edge GPUs, the simulation speed can possibly be further reduced by a factor of seven; (4) Conclusions: The parallelized simulator presented in this paper can perform large-scale microsimulations in a reasonable time on readily available and inexpensive computer hardware. This means microsimulations could now be used in new application fields such as activity-based demand generation, reinforced AI learning, traffic forecasting, or crisis response management. Full article

Intersectional oppression and invisibility are primary drivers of cognitive and mental health disparities that affect Black women’s wellness. Older Black women additionally experience compounding effects of ageism, which may place them at increased risk for a decline in cognitive functioning and mental wellness. To date, limited strengths-based, culturally relevant programming has focused on aging Black women. Fewer have incorporated Black women elders into conversations on Black liberation and the transformational change needed to create possible futures rooted in equity, healing, and health. This manuscript describes the inception and development of Radical Imagination, a creative aging program for Black women in the San Francisco Bay Area. Over ten weeks, 42 Black women (M age = 73.6, SD = 6.20; range: 58–85 years old) participated in the program, which incorporated brain and mental health education, art-making, storytelling, and photography. Grounded in principles of Afrofuturism and radical healing, participants explored past narratives of Black women and created a collective vision for a future that centers on Black women’s needs. Approximately 54.8% of participants attended more than one workshop. Upon program completion, exit surveys indicated that participants reported a moderate level of hopefulness about their ability to shape the future. Respondents reported overall satisfaction with the workshop series. We conclude with reflections on our process and recommendations for ways to support aging Black women using Afrofuturism and the arts. Full article

Genome editing technologies have revolutionized the production of microorganisms, plants, and animals with phenotypes of interest to agriculture. Editing previously sequenced genomes allows for the punctual, discreet, precise, and accurate alteration of DNA for genetic analysis, genotyping, and phenotyping, as well as the production of edited organisms for academic and industrial purposes, among many other objectives. In this context, genome editing technologies have been causing a revolution in Brazilian agriculture. Thanks to the publication of Normative Resolution No. 16 (in Portuguese Resolução Normativa No. 16-RN16) in 2018, Brazilian regulatory authorities have adapted to the new genetic manipulation technologies available to the scientific community. This review aims to describe the effects of updates to the regulatory framework for edited organisms in Brazil and to point out their impacts on research and development of emerging technologies in the Brazilian agricultural sector. The implementation of RN16 rationalized the regulatory aspects regarding the production, manipulation, exploration and commercial release of edited organisms and led to the faster, cheaper and safer obtaining of edited technologies, which are more productive and better adapted to different environmental conditions in Brazil. Full article

Conjugated linoleic acid (CLA) is a bioactive compound known for its anti-inflammatory, anti-carcinogenic, and metabolic effects, with growing interest in its role in supporting bone health. Preclinical studies, particularly those involving the t10c12 isomer, have shown that CLA can enhance bone mineral density (BMD) by enhancing bone formation and reducing bone resorption, indicating its potential as a therapeutic agent to improve bone health. However, clinical trials have yielded inconsistent results, underscoring the difficulty in translating animal model successes to human applications. A major challenge is CLA’s low water solubility, poor absorption, and limited bioavailability, which restrict its therapeutic effectiveness. To address these issues, nanoparticle-based delivery systems have been proposed to improve its solubility, stability, and resistance to oxidative damage, thereby enhancing its bioactivity. Recent studies also suggest that electrical stimulation can stimulate bone regeneration by promoting bone cell proliferation, differentiation, and adherence to scaffolds. This review explores the combined use of CLA supplementation and electrical stimulation as a novel approach to improving bone health, particularly in osteoporosis management. By integrating CLA’s biological effects with the regenerative potential of electrical stimulation, this multimodal strategy offers a promising method for enhancing bone restoration, with significant implications for clinical applications in bone health. Full article