Search Results (422)

Urban parks are central to public health and equity, yet less is known about how park travel distance, park “attractor” types, and time-of-day visitation rhythms co-evolved through and after the COVID-19 pandemic. Using anonymized smartphone mobility traces for public parks in Las Vegas, USA (2018–2022), we construct weekly origin–destination flows between census block groups (CBGs) and parks and link origins to socio-economic indicators. We first estimate visitor-weighted mean travel distance with a segmented time-series model that allows pandemic-related breakpoints. Results show that average park-trip distance (≈8.4 km pre-pandemic), including a substantial share of long-distance trips (≈52% of visits), contracted sharply at the onset of COVID-19, and that both travel radii and seasonal excursion peaks only partially rebounded by 2022. Next, cross-sectional OLS/WLS models (R² ≈ 0.08–0.14) indicate persistent socio-spatial disparities: CBGs with higher educational attainment and larger shares of Black and Hispanic residents are consistently associated with shorter park-trip distances, suggesting constrained recreational mobility for socially disadvantaged groups. We then identify a stable two-type park typology—local versus regional attractors—using clustering on origin diversity and long-distance share (silhouette ≈ 0.46–0.52); this typology is strongly related to visitation volume and temporal usage profiles. Finally, mixed-effects models of evening and late-night visit shares show that regional attractors sustain higher nighttime activity than local parks, even as citywide evening/late-night visitation dipped during the mid-pandemic period and only partly recovered thereafter. Overall, our findings reveal a durable post-pandemic re-scaling of park use toward more proximate, CBG-embedded patterns layered on enduring inequities in access to distant, destination-oriented parks. These insights offer actionable evidence for equitable park planning, targeted investment in high-need areas, and time-sensitive management strategies that account for daytime versus nighttime use. Full article

Actinium-225 (²²⁵Ac) has emerged as a pivotal alpha-emitter in modern radiopharmaceutical therapy, offering potent cytotoxicity with the potential for precise tumour targeting. Accurate, patient-specific image-based dosimetry for ²²⁵Ac is essential to optimize therapeutic efficacy while minimizing radiation-induced toxicity. Establishing a robust dosimetry workflow is particularly challenging due to the complex decay chain, low administered activity, limited count statistics, and the indirect measurement of daughter gamma emissions. Clinical single-photon emission computed tomography/computed tomography protocols with harmonized acquisition parameters, combined with robust volume-of-interest segmentation, artificial intelligence (AI)-driven image processing, and voxel-level analysis, enable reliable time-activity curve generation and absorbed-dose calculation, while reduced mixed-model approaches improve workflow efficiency, reproducibility, and patient-centred implementation. Cadmium zinc telluride-based gamma cameras further enhance quantitative accuracy, enabling rapid whole-body imaging and precise activity measurement, supporting patient-friendly dosimetry. Complementing these advances, the cerium-134/lanthanum-134 positron emission tomography in vivo generator provides a unique theranostic platform to noninvasively monitor ²²⁵Ac progeny redistribution, evaluate alpha-decay recoil, and study tracer internalization, particularly for internalizing vectors. Together, these technological and methodological innovations establish a mechanistically informed framework for individualized ²²⁵Ac dosimetry in targeted alpha therapy, supporting optimized treatment planning and precise response assessment. Continued standardization and validation of imaging, reconstruction, and dosimetry workflows will be critical to translate these approaches into reproducible, patient-specific clinical care. Full article

Background/Objectives: Stereotactic body radiation therapy (SBRT) is widely used for small lung tumors, but the physics of electron transport in low-density lungs remains incompletely understood. This study quantifies the effect of lung density on dosimetry for small lesions. Methods: To study the dosimetric parameters a pseudo patient option was chosen. A lung SBRT patient with a central lesion was modeled in the Eclipse treatment planning system using the AAA algorithm. Three target sizes (1.0, 1.5, and 2.0 cm) were planned with lung densities overridden from 0.1 to 1.0 g/cm³. Standard SBRT constraints were applied, and dosimetry indices (CI, HI, GI), maximum dose, and MU/Gy were recorded to see the pattern. Results: Dose–volume histograms (DVHs) showed marked dependence on both lesion size and lung density. Lower densities produced higher maximum doses (up to 135% at 0.1 g/cm³), steeper DVH tails, and significantly increased MU/Gy. Conformity was achievable in all cases, but at the cost of degraded homogeneity and gradient indices. At higher density (1.0 g/cm³), maximum dose values fell to 108–110% which is typical in non-lung cases. Conclusions: SBRT planning in low-density lungs requires substantially higher MU and results in greater dose spillage despite acceptable conformity. These findings highlight the importance of considering density effects when comparing clinical outcomes across institutions and selecting optimal plans, where minimizing MU/Gy may reduce unnecessary dose burden. Full article

Low-carbon agriculture (ABC—from the acronym in Portuguese) encompasses techniques that reduce carbon emissions while maintaining productivity and profitability. Among these, the restoration of degraded pastures is a major focus of the Brazilian ABC policy, achieved through improved pasture management or crop–livestock integration. This study analyzed the relationship between ABC credit and improvements in pasture vigor in the municipalities of Minas Gerais from 2015 to 2022, considering the carbon-mitigation potential of each region. We evaluated whether credit resources were directed toward areas with greater mitigation potential and whether this investment contributed to pasture recovery. Composite indexes were developed to represent credit investment, pasture dynamics, and theoretical carbon removal potential, followed by spatial mapping and correlation analysis. The results show that ABC credit was strongly concentrated in regions with high carbon-sequestration potential, especially Triângulo Mineiro and Alto Paranaíba, indicating a generally effective targeting of resources toward areas with greater mitigation potential. Correlation analysis also indicates a positive, although moderate, association between credit volume and pasture improvement at the municipal level. Although initial results indicated more substantial improvements in pasture vigor in lower-credit regions such as North of Minas, Jequitinhonha, and Mucuri Valley (with relative increases reaching up to 300%), an additional analysis considering the initial vigor level (baseline) revealed that these gains are strongly affected by initial pasture conditions. From a policy perspective, these findings highlight the importance of rural credit as a driver of sustainable technology adoption, while also showing that baseline conditions, technical assistance, and other public or private incentives can significantly influence restoration outcomes. Strengthening credit allocation criteria, improving technical support, and integrating carbon-mitigation indicators into regional planning could enhance environmental effectiveness. Full article

Background: Despite the availability of contouring guidelines and advanced imaging modalities, interobserver variability (IOV) in the delineation of the planning target volume and organs at risk remains a critical factor influencing treatment quality in radiotherapy. The aim of this study was to examine variations in contour delineation with respect to anatomical landmarks, as well as differences in the inclusion of lymph node levels within the PTV. Methods: Ten senior radiation oncologists from six different institutions participated in the study and contoured PTV1, PTV2 and 16 OARs in a patient with oropharyngeal carcinoma. Interobserver variation was quantified by volume statistics such as mean, standard deviation (SD) and ranges, as well as using coefficient of variance (CoV) and conformity index (CI). Results: High agreement was observed in the inclusion of the ipsilateral lymph node levels Ib–IVa and VIIa+b, whereas notable discrepancies were identified in the delineation inclusion of the cervical triangle group and lateral supraclavicular nodes. Regarding OARs, the greatest variability was observed in the delineation of the left and right inner ear, with volume ranges of 0.12–2.84 cm³ and 0.11–2.38 cm³, respectively. Conclusions: This study reaffirms the presence of significant interobserver variability in the delineation of PTVs and OARs in patients with oropharyngeal carcinoma. Especially inclusion of elective lymph node levels and definition of margins around the gross tumor volume are substantial factors for IOV. By emphasizing structured anatomical assessment as a standard approach, variability can be minimized, treatment consistency enhanced, and ultimately, patient outcomes improved. Full article

The global cancer burden continues to increase worldwide. Among the various treatment options, radiotherapy (RT), which employs high-energy ionizing radiation to destroy cancer cells, is one of the primary modalities for cancer. However, increasing the absorbed dose to the target volume also increases the risk of damage to surrounding healthy tissues. This radiation-induced toxicity to normal tissues limits the desirable dosage that can be delivered to the tumor, thereby constraining the effectiveness of radiation therapy in achieving tumor control. FLASH radiotherapy (FLASH-RT) has emerged as a promising technique due to its biological advantages. FLASH-RT involves the delivery of radiation at an ultra-high dose rate (≥40 Gy/s). Unlike conventional RT, FLASH-RT achieves comparable tumor control rates while significantly reducing damage to surrounding normal tissues, a phenomenon known as the FLASH effect. Although the mechanism behind the FLASH effect is not fully understood, this approach shows considerable promise for future cancer treatment. The development of specialized treatment planning systems (TPS) becomes imperative to facilitate the clinical implementation of FLASH-RT from experimental studies. These systems must account for the unique characteristics of FLASH-RT, including ultra-high dose rate delivery and its distinctive radiobiological effects. Critical reassessment and optimization of treatment planning protocols are essential to fully leverage the therapeutic potential of the FLASH effect. This review examines key considerations for the TPS development of electron and proton FLASH-RT, including electron and proton FLASH techniques, biological models, crucial beam parameters, and dosimetry, providing essential insights for optimizing TPS and advancing the clinical implementation of this promising therapeutic modality. Full article

The environmental impacts of road transport, in particular air pollution and noise, are receiving increasing attention in urban and regional planning, as they can not only predict vehicle movements but also provide detailed information on traffic volumes and speed distributions, which are indispensable for effective regulation, targeted interventions and health-conscious urban planning. This study presents an emission calculation module that can be integrated into traffic models and provides detailed estimates of pollutants emitted by road vehicles. The developed module builds on the COPERT methodology, which accounts not only for exhaust emissions such as ${\mathrm{CO}}_2$, ${\mathrm{NO}}_x$ and PM, but also for non-exhaust emissions from brake wear, tyre wear, road abrasion and evaporation. The presented system has an open architecture, enabling further customisation, particularly when local measured data are available. This contributes to building a stronger, data-driven link between transport planning and environmental protection. Full article

This study develops a conjunctive-use framework that couples a surface water allocation model with the MODFLOW groundwater model to evaluate the interactions between surface water operations and groundwater recharge and pumping. The framework enables coordinated surface–groundwater management through iterative feedback between allocation decisions and groundwater responses. Three representative managed aquifer recharge cases in Taiwan are examined, each reflecting a distinct operational logic: (1) a space-for-time strategy that extends wet-season benefits through distributed recharge using irrigation surplus; (2) a centralized support–distributed feedback approach in subsidence-prone areas, where concentrated surface water is delivered to targeted zones while maintaining flexibility for upstream allocation; and (3) a time-for-volume mechanism that converts short-duration flood events into stable, long-term baseflow supply. The simulation results show that these strategies reduce downstream irrigation deficit ratios (e.g., from 0.58 to 0.22), raise groundwater levels by up to approximately 3.5 m in subsidence-sensitive zones, and substantially enhance drought resilience by reducing extreme reservoir depletion during prolonged dry periods. Overall, the proposed framework provides quantitative evidence and a practical planning tool for surface water-oriented conjunctive use, supporting more sustainable and resilient multi-source water management. Full article

To address issues such as slow response speed and low detection accuracy in fallen walnut picking robots in complex orchard environments, this paper proposes a detection and path planning method that integrates star-shaped lightweight convolution with NDT-RRT. The method includes the improved lightweight detection model YOLO-FW and an efficient path planning algorithm NDT-RRT. YOLO-FW enhances feature extraction by integrating star-shaped convolution (Star Blocks) and the C3K2 module in the backbone network, while the introduction of a multi-level scale pyramid structure (CA_HSFPN) in the neck network improves multi-scale feature fusion. Additionally, the loss function is replaced with the PIoU loss, which incorporates the concept of Inner-IoU, thus improving regression accuracy while maintaining the model’s lightweight nature. The NDT-RRT path planning algorithm builds upon the RRT algorithm by employing node rejection strategies, dynamic step-size adjustment, and target-bias sampling, which reduces planning time while maintaining path quality. Experiments show that, compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively, with a volume of only 3.62 MB and a 30.65% reduction in the number of parameters. The NDT-RRT algorithm reduces search time by 87.71% under conditions of relatively optimal paths. Furthermore, a detection and planning system was developed based on the PySide6 framework on an NVIDIA Jetson Xavier NX embedded device. On-site testing demonstrated that the system exhibits good robustness, high precision, and real-time performance in real orchard environments, providing an effective technological reference for the intelligent operation of fallen walnut picking robots. Full article

Background and Objectives: The bulboclitoral complex (BCC) is an essential organ for female sexual health. However, it is not defined as an organ at risk in any guideline defining target volumes in radiotherapy of gynecological cancers, and there is no information about dose constraint. Materials and Methods: Simulation computed tomography scans of 20 patients diagnosed with locally advanced cervical cancer were used retrospectively. The volumetric modulated arc therapy treatment plan with a total dose of 45 Gy in 25 fractions was created using the planning target volume (PTV)-standard, which was created without considering the BCC, and the PTV-BCC spared, which were contoured and included in the optimization. Bulboclitoral complex doses in PTV-standard and PTV-BCC spared plans were compared using the paired simple t test. Results: Median BCC volume was 17.6 cm³ (11.20–25.50). Bulboclitoral complex maximum dose (Dmax) was median 49.07 Gy (48.49–50.25) and 28.81 Gy (18.14–44.61) in the PTV-standard and PTV-BCC spared plans, respectively, and the BCC Dmax was statistically significantly lower in the PTV-BCC spared plan (p < 0.001). When comparing BCC percentage of volume receiving 45 Gy (V45), the median values for PTV-standard and PTV-BCC spared plans were 37.5% (13.3–82.6) and 0%, respectively (p ≤ 0.001). Conclusions: The bulboclitoral complex can be dosimetrically protected from radiation by contouring and optimizing it as an organ at risk in the radiotherapy plan. The clinical effects of protecting the BCC from radiation as an organ at risk on sexual health need to be investigated. Full article

The dual imperative of mitigating carbon emissions and maximizing hydrocarbon recovery has amplified global interest in carbon capture, utilization, and storage (CCUS) technologies. These integrated processes hold significant promise for achieving net-zero targets while extending the productive life of mature oil reservoirs. However, their effectiveness hinges on a nuanced understanding of the complex interactions between geological formations, reservoir characteristics, and injection strategies. In this study, a comprehensive machine learning-based framework is presented for estimating CO₂ storage capacity and enhanced oil recovery (EOR) performance simultaneously in subsurface reservoirs. The methodology combines simulation-driven uncertainty quantification with supervised machine learning to develop predictive surrogate models. Simulation results were used to generate a diverse dataset of reservoir and operational parameters, which served as inputs for training and testing three machine learning models: Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN). The models were trained to predict three key performance indicators (KPIs): cumulative oil production (bbl), oil recovery factor (%), and CO₂ sequestration volume (SCF). All three models exhibited exceptional predictive accuracy, achieving coefficients of determination (R²) greater than 0.999 across both training and testing datasets for all KPIs. Specifically, the Random Forest and XGBoost models consistently outperformed the ANN model in terms of generalization, particularly for CO₂ sequestration volume predictions. These results underscore the robustness and reliability of machine learning models for evaluating and forecasting the performance of CO₂-EOR and sequestration strategies. To enhance model interpretability and support decision-making, SHapley Additive exPlanations (SHAP) analysis was applied. SHAP, grounded in cooperative game theory, offers a model-agnostic approach to feature attribution by assigning an importance value to each input parameter for a given prediction. The SHAP results provided transparent and quantifiable insights into how geological and operational features such as porosity, injection rate, water production rate, pressure, etc., affect key output metrics. Overall, this study demonstrates that integrating machine learning with domain-specific simulation data offers a scalable approach for optimizing CCUS operations. The insights derived from the predictive models and SHAP analysis can inform strategic planning, reduce operational uncertainty, and support more sustainable oilfield development practices. Full article

Pharmaceutical manufacturing and logistics rely on accurate prediction and decision making to safeguard product quality, delivery reliability, and patient outcomes. Despite rapid advances in artificial intelligence (AI) and machine learning (ML), few studies benchmark model performance across the diverse operational demands of global pharmaceutical supply chains. Predictive setbacks contribute to financial losses, reduced supply chain efficacy, and potential adverse health consequences, yet understanding these failures offers firms opportunities to refine strategy and strengthen resilience. Drawing on 1.2 million shipments spanning 39 countries, we compare traditional statistical models (ARIMA), ensemble methods (random forests, gradient boosting), and deep neural networks (LSTM, GRU, CNN, ANN) across pricing, demand forecasting, vendor management, and shipment planning. Gradient boosting produced the strongest pricing performance, while ARIMA delivered the lowest demand-forecasting errors but with limited explanatory power; neural networks captured nonlinear demand shocks and achieved superior maintenance-risk classification. We also identified three vendor performance clusters—high-performing, cost-efficient, and mixed-reliability vendors—enabling firms to better align shipment criticality with vendor capabilities by prioritizing high performers for urgent deliveries, leveraging cost-efficient vendors for non-urgent volumes, and managing mixed performers through targeted oversight. These insights highlight the value of our evidence-based roadmap for selecting algorithms in high-stakes healthcare logistics, in rapidly evolving, technologically complex global contexts where increasing algorithmic sophistication elevates the standards for safer, smarter pharmaceutical supply chains. Full article

Background and Objectives: The axillary lateral vessel thoracic junction (ALTJ) is a key lymphatic drainage pathway for the arm and a potential structure to spare during regional nodal irradiation (RNI) to reduce lymphedema risk in breast cancer patients. This study aims to demonstrate the feasibility of ALTJ-sparing radiation therapy (RT) planning using Tomotherapy. Materials and Methods: Ten breast cancer patients who had undergone axillary lymph node dissection and whose dissected axillary levels were excluded from the RNI target volume were included. A TomoDirect intensity-modulated RT plan was generated at a dose of 50 Gy in 25 fractions. The dissected axilla was not designated as an organ at risk (OAR) in the original treatment plan. For this study, the axillary lymph node level I (AXL1) and the ALTJ were delineated retrospectively, with the ALTJ considered an OAR in the newly generated study plan. A total of 20 RT plans (10 per group) were statistically compared using various dose-volume parameters. Results: Compared to the original plans, the study plans with ALTJ as an OAR significantly reduced the incidental dose to both the ALTJ (mean: 41.7 ± 3.4 Gy vs. 27.2 ± 1.3 Gy; p = 0.005) and the AXL1 (mean: 43.9 ± 2.0 Gy vs. 37.7 ± 1.9 Gy; p = 0.005). All other dosimetric parameters (V25Gy, V35Gy, V40Gy, Dmin, Dmax) for the ALTJ were also significantly lower in the study plans. This ALTJ sparing was achieved while maintaining all required dose-volume constraints for target volumes and standard OARs such as the lung and heart. Conclusions: This study demonstrates that simply excluding the dissected axilla from the target volume without designating it as an OAR still results in a substantial incidental dose to this region. Our findings also show the feasibility of using Tomotherapy to selectively spare the axilla, particularly the ALTJ subregion of AXL1, which is critical for lymphedema risk in breast cancer patients. Full article

Background: Stereotactic radiotherapy (SRT) is a promising treatment option for patients with multiple brain metastases (BMs). Using one isocenter instead of a separate isocenter for each BM can reduce the treatment time. This work compares the calculated dose in the treatment planning system with the measured dose using film dosimetry of single-isocenter multi-target (SIMT) SRT for multiple BM. Methods: Fifty patients with 4 to 18 BMs (median = 6, in total 356 BMs) were treated with a single-isocenter non-coplanar LINAC-based treatment with six VMAT arcs. Treatment was performed using RayStation and Elekta Versa HD with Agility multileaf collimator, including a 6D robotic couch. Patient-specific QA measurements were performed with an in-house developed phantom using three layers of GafChromic EBT3 film. Film measurements were analyzed in DoseLab using global gamma with 3% and 1 mm distance-to-agreement criteria. Additionally, secondary dose calculations in Mobius3D were performed with similar gamma criteria. Results: The mean total Paddick conformity index and gradient index were 0.7 ± 0.10 and 5.2 ± 1.9, respectively. Monitor units used were 6321 ± 2510, and mean irradiation time was 600 ± 90 s. The mean global gamma passing rate for all measured films was 94.5 ± 4.6% with 3% and 1 mm criteria, while that of the dose calculations in Mobius3D was 98.2 ± 1.2% with the same criteria. A dependence of gamma passing rates of film measurements on the total PTV volume was observed, whereas such dependence was minimal for Mobius3D. Conclusions: The results demonstrate good agreement between the TPS, film measurements, and independent dose calculations, supporting the dosimetric accuracy of single-isocenter multi-target SRT for treating multiple BMs. Full article

Purpose: To improve the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT), we developed a hybrid artificial intelligence (AI) model that integrates deep learning radiomics (DLR), handcrafted radiomics (HCR), clinical features, and dose–volume histogram (DVH) parameters, aiming to enhance the early identification of high-risk individuals and support personalized prevention strategies. Methods: A retrospective cohort of 156 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital (2018–2023) was analyzed; 148 patients were eligible after exclusions, with RD graded according to the RTOG criteria. Clinical variables and 12 DVH indices were collected, while HCR features were extracted via PyRadiomics. DLR features were derived from a pretrained VGG16 network across four input designs: original CT images (DLR_Original), a 5 mm subcutaneous region (DLR_Skin5mm), a planning target volume with a 100% prescription dose (DLR_PTV100%), and a subcutaneous region receiving ≥ 5 Gy (DLR_V5Gy). The features were preselected via ANOVA (p < 0.05), followed by Boruta–SHAP refinement across 11 feature sets. Predictive models were built via logistic regression, random forest, gradient boosting decision tree, and stacking ensemble (SE) methods. Explainability was assessed via SHapley Additive exPlanations (SHAPs) and gradient-weighted class activation mapping (Grad-CAM). Results: Among the 148 patients, 49 (33%) developed Grade ≥ 2 RD. The DLR models outperformed the HCR models (AUC = 0.72 vs. 0.66). The best performance was achieved with DLR_V5Gy + clinical + DVH features, yielding an AUC = 0.76, recall = 0.68, and F1 score = 0.60. SE consistently surpassed single classifiers. SHAP identified convolutional DLR features as the strongest predictors, whereas Grad-CAM focused attention on subcutaneous high-dose regions, which was consistent with the clinical RD distribution. Conclusions: The proposed hybrid AI framework, which integrates DLR, clinical, and DVH features, provides accurate and explainable predictions of Grade ≥ 2 RD after VMAT in breast cancer patients. By combining ensemble learning with XAI methods, the model offers reliable high-risk stratification and potential clinical utility for personalized treatment planning. Full article