Search Results (32)

Safe and resilient operation of liquefied petroleum gas (LPG) storage and distribution stations is important to the sustainability of fuel infrastructure, yet boiling liquid expanding vapor explosion (BLEVE) prevention requires time-dependent barrier analysis and a clear separation between prevention priorities and post-rupture exposure. This study develops a dynamic–spatial screening framework that integrates evidence-graded basic events, a priority-AND dynamic fault tree, a continuous-time Markov chain (CTMC), memory-efficient Monte Carlo trajectory simulation, modeled-area prevention prioritization, and tank-origin relative-exposure screening. A one-million-trajectory accelerated numerical baseline produced 112,693 BLEVE-conditioned trajectories. Weld cracking and fire-pump failure had the highest dynamic criticality because they act in later barrier-degradation stages, whereas high-wind spread had the greatest conditional involvement. Tank structure had the highest modeled-area prevention priority. Receiver rankings depended on the assumed layout: P5 ranked first under the baseline schematic coordinates, whereas P2 ranked first under the illustrative metric-coordinate scenario across the tested distance-attenuation kernels. Convergence, acceleration-factor, evidence-grade, weight, recovery, alternative-path, static-importance, coordinate, and kernel sensitivities were evaluated. Prevention-priority scores and normalized exposure indices are reported separately and are not interpreted as calibrated accident frequency, physical dose, expected loss, or quantitative risk. By supporting targeted inspection, maintenance, monitoring, and emergency-resource allocation, the framework can contribute to safer, more resilient, and more sustainable operation of LPG infrastructure. Full article

Non-destructive assessment of wood quality in standing trees is increasingly important for value-based harvesting, precision forestry, and large-scale monitoring. Yet most operational approaches rely on destructive sampling or plot-level measurements that cannot be feasibly extended across extensive forest areas. A central barrier to scalable assessment is the spatial misalignment between external structural measurements and internal wood-quality responses which introduces systematic bias when conventional regression methods are applied. We introduce Regression based on Misaligned Covariates (RMC), a statistical framework that reconciles covariates and responses measured at different spatial locations by combining the Law of Total Expectation with kernel-based estimation of marginal relationships. RMC recovers height-dependent conditional means without requiring one-to-one spatial correspondence. RMC is demonstrated using two contrasting conifers, eastern white pine (Pinus strobus) and red pine (Pinus resinosa), with external covariates derived from high-precision manual measurements compatible with LiDAR-based structural characterization. Across leave-one-tree-out cross-validation, linear functional forms frequently outperformed the Normal model with a few exceptions, producing smoother and biologically interpretable knot-volume profiles aligned with known species-specific crown architecture. Model performance varied by stem zone and kernel choice when evaluated against the height-only baseline (m-hat). For white pine, the linear kernel achieved the lowest error in the clear stem (1.04 ± 0.04) and the Normal kernel performed best in the living crown (1.09 ± 0.08). For red pine, the linear kernel minimized error in the clear stem (1.28 ± 0.49), while the quadratic kernel yielded the lowest mean error in the living crown (1.24 ± 0.05). The resulting cumulative knot volume profiles captured species-specific differences in knot accumulation and aligned with known patterns of crown architecture and stem form. RMC establishes a mathematically formal foundation for estimating internal wood-quality attributes from external structural metrics for misaligned covariates. This framework offers a theoretical framework for future integration with automated LiDAR-derived datasets and provides a conceptual step toward non-destructive, stand-level wood-quality assessment. Full article

Precision livestock farming (PLF) is hindered by high costs, infrastructure demands, and complex deployment. To address these barriers, we developed CABRA, an open-hardware wearable system for real-time behaviour monitoring in pasture-based livestock. The collar-mounted device integrates a 6-axis IMU, a GPS, and a low-power ESP32 microcontroller within a modular architecture, using the routerless ESP-NOW protocol to transmit data directly to a base station—eliminating reliance on network infrastructure or cloud connectivity. The system supports both synchronised data logging for video annotation and real-time embedded behaviour classification via an optimized decision-tree pipeline deployed directly on the microcontroller. Field trials with dairy goats confirmed robust hardware performance, minimal animal disturbance, and reliable communication over 100 m. A two-stage evaluation revealed that while the extracted IMU features are highly discriminative (achieving F1 > 0.99 under window-level validation), cross-animal generalization remains challenging (macro F1 = 0.31 under rigorous animal-level partitioning), primarily due to the “sensor placement effect” and domain shift between individuals. These results honestly quantify the current limitations of uncalibrated wearable livestock sensing while validating the functional feasibility of edge-based inference. All design assets—CAD files, schematics, firmware, and data pipelines—are openly released to ensure full reproducibility and community-driven adaptation for diverse PLF applications. Full article

Under the global energy transition, liquefied petroleum gas (LPG) remains an important transitional fuel. However, persistent safety risks in storage and transportation continue to limit its sustainable development. This study aims to evaluate the sustainability of China’s LPG storage and transportation system and identify practical improvement pathways. A “1+4” C-STSM multidimensional framework was developed by combining accident fault-tree analysis, comparative review of domestic and international standards, and a systematic assessment of storage, transportation, monitoring, and safety technologies. The results show that the sustainability of LPG systems depends on the coordinated performance of infrastructure, transportation, monitoring, and safety barriers across the full supply chain. China has made progress in engineering facilities and safety management, but still faces weaknesses in intrinsic safety, barrier integrity, intelligent monitoring, and life-cycle governance. The main gap with international advanced practice lies in insufficient system integration rather than the lack of basic technologies. Improving LPG sustainability requires a coordinated pathway that combines safer infrastructure, intelligent monitoring, stronger barrier management, and better regulatory coordination. Such an approach can enhance industrial safety while supporting low-loss, low-emission energy transition. Full article

The effective management of fracturing flowback wastewater is critical to oil and gas production sustainability, while its complex and rapidly evolving rheology poses a significant barrier to monitoring and targeted treatment. Traditional offline sampling methods suffer from measurement latency, failing to capture real-time dynamic changes in treatment reactors. To address these limitations, this study develops a novel machine learning-assisted in situ monitoring system integrating ultrasonic time-domain reflectometry (UTDR) to characterize fluid components, concentrations, and viscosity simultaneously. Specifically, the random forest model achieved the highest accuracy (88.0%) in component identification among three tree-based algorithms, while support vector classification (SVC) effectively discriminated concentration levels with an accuracy of 82.4%. For viscosity prediction, the 1D-convolutional neural network (1D-CNN) demonstrated superior performance, achieving an R² of 0.972. Crucially, interpretability analyses (SHAP and Grad-CAM) confirmed that model decisions align with hydroacoustic principles of attenuation and viscous damping. In dynamic enzymatic degradation tests, the system successfully tracked rapid viscosity transitions with a relative error of less than 13%. This approach provides a high-resolution, cost-effective solution for the intelligent monitoring of fracturing flowback wastewater. Full article

Poplars (Populus L.) are fast-growing, widely distributed trees with high ecological, economic, and climate-mitigation value, making them central to diverse agroforestry systems worldwide. This study presents a comprehensive bibliometric and content-based review of global poplar-based agroforestry research, using Scopus and Web of Science databases and a PRISMA-guided screening process to identify 496 peer-reviewed publications, covering publications from 1987 to 2024. Results show a steady rise in scientific output, with a notable acceleration after 2013, dominated by agriculture, forestry, and environmental sciences, with strong international contributions and research themes focused on productivity, carbon sequestration, biodiversity, and economic viability. A wide range of Populus species and hybrids is employed globally, supporting functions from crop production and soil enhancement to climate mitigation and ecological restoration. Poplar-based systems offer substantial benefits for soil health, biodiversity, and carbon storage, but also involve trade-offs related to tree–crop interactions, such as competition for light reducing understory crop yields in high-density arrangements, management intensity, and regional conditions. Poplars provide a wide array of provisioning, regulating, and supporting ecosystem services, from supplying food, fodder, timber, and biomass to moderating microclimates, protecting soil and water resources, and restoring habitats, while supporting a broad diversity of agricultural and horticultural crops. However, several critical gaps—including a geographic research imbalance, socio-economic and adoption barriers, limited understanding of tree–crop interactions, and insufficient long-term monitoring—continue to constrain widespread adoption and limit the full realization of the potential of poplar-based agroforestry systems. Full article

Rapid urban expansion has heightened the demand for accurate, scalable, and real-time methods to assess tree health and the provision of ecosystem services. Urban trees are the major contributors to air-quality improvement and climate change mitigation; however, their monitoring is mostly constrained to inherently subjective and inefficient manual inspections. In order to break this barrier, we put forward a lightweight multimodal deep-learning framework that fuses RGB imagery with environmental and biometric sensor data for a combined evaluation of tree-health condition as well as the estimation of the daily oxygen production and CO₂ absorption. The proposed architecture features an EfficientNet-B0 vision encoder upgraded with Mobile Inverted Bottleneck Convolutions (MBConv) and a squeeze-and-excitation attention mechanism, along with a small multilayer perceptron for sensor processing. A common multimodal representation facilitates a three-task learning set-up, thus allowing simultaneous classification and regression within a single model. Our experiments with a carefully curated dataset of segmented tree images accompanied by synchronized sensor measurements show that our method attains a health-classification accuracy of 92.03% while also lowering the regression error for O₂ (MAE = 1.28) and CO₂ (MAE = 1.70) in comparison with unimodal and multimodal baselines. The proposed architecture, with its 5.4 million parameters and an inference latency of 38 ms, can be readily deployed on edge devices and real-time monitoring platforms. Full article

Indoor Air Quality (IAQ) in educational environments is a critical determinant of students’ health, well-being, and learning performance, with inadequate ventilation and pollutant accumulation consistently associated with respiratory symptoms, fatigue, and impaired cognitive outcomes. Conventional monitoring approaches—based on periodic inspections or subjective perception—provide only fragmented insights and often underestimate exposure risks. Artificial intelligence (AI) offers a transformative framework to overcome these limitations through sensor calibration, anomaly detection, pollutant forecasting, and the adaptive control of ventilation systems. This review critically synthesizes the state of AI applications for IAQ management in educational environments, drawing on twenty real-world case studies from North America, Europe, Asia, and Oceania. The evidence highlights methodological innovations ranging from decision tree models integrated into large-scale sensor networks in Boston to hybrid deep learning architectures in New Zealand, and regression-based calibration techniques applied in Greece. Collectively, these studies demonstrate that AI can substantially improve predictive accuracy, reduce pollutant exposure, and enable proactive, data-driven ventilation management. At the same time, cross-case comparisons reveal systemic challenges—including sensor reliability and calibration drift, high installation and maintenance costs, limited interoperability with legacy building management systems, and enduring concerns over privacy and trust. Addressing these barriers will be essential for moving beyond localized pilots. The review concludes that AI holds transformative potential to shift school IAQ management from reactive practices toward continuous, adaptive, and health-oriented strategies. Realizing this potential will require transparent, equitable, and cost-effective deployment, positioning AI not only as a technological solution but also as a public health and educational priority. Full article

The codling moth Cydia pomonella (L.) represents a substantial threat to the apple tree industry, with its cellular content being agronomically vital as it serves as the final immunological and toxicological barrier of the pest. Key hemocyte types identified in the hemolymph include plasmatocytes, granulocytes, spherulocytes, and oenocytoids. Hemolymph samples were in vitro suspended in various salt buffers (physiological saline, phosphate saline buffer (PBS) and Galleria mellonella anticoagulant buffer) to determine the most suitable one for agricultural monitoring purposes. The pH influenced the total hemocyte counts and the type of cells that adhered to the slides. PBS (pH 6.5) was found to be optimal for such studies due to its high levels of cellular attachment, cell viability, absence of melanization, and cellular degeneration effects. The supplementation of 5% CaCl₂ to PBS did not enhance the functional utility of the buffer. The in vivo bacterial challenge of larval hemolymph with 4 × 10⁸ sp/mL Bacillus subtilis provided complete clearance from the microbial invader within 30 min. Hemocytes released antimicrobial lysozyme as part of their innate immune responses. Hemocytic examination of larvae as an agricultural practice is strongly recommended for baseline insecticide resistance avoidance and predictive efficiency of integrated pest management in the apple farm. Full article

Background: Primary sclerosing cholangitis (PSC) is a rare chronic liver disease characterised by bile duct strictures. Magnetic resonance cholangiopancreatography (MRCP) is the principal imaging modality for diagnosis; however, its interpretation is subjective. Quantitative MRCP (MRCP+) provides quantitative assessment of the biliary anatomy and can support objective disease assessment. We evaluated the potential impact, feasibility, and perceived usefulness that MRCP+ would have on PSC patient management. Methods: Alongside systematic evaluation of UK and European clinical guidelines on PSC management, semi-structured interviews with 16 stakeholders were conducted. The Lean Assessment Process methodology was used to assess potential impact and feasibility of adopting MRCP+ for the PSC care pathway within the NHS. Price as a barrier to adoption was investigated to evaluate perceptions between technology cost and adoption. Perceived ease of use and perceived trust were calculated and used to evaluate perceived usefulness (PU). Results: For PSC management, MRCP (81%) scored higher than liver biopsy (68%) and ERCP (50%) due to its non-invasive nature. There was good internal consistency between responders on the relationship between price point and the use of MRCP+ to support diagnosis (CA:0.836) and monitoring (CA:0.904). A price point of up to GBP 500 was unlikely to be a barrier for adoption. The overall perceived usefulness for MRCP+ for patient management was 74%. Conclusions: There is strong interest in using MRCP+ to support PSC management. MRCP+ has the potential to address unmet needs including reducing subjectivity, measurement of the whole biliary tree and objectively measuring biliary disease progression. Full article

With the expansion of the scale of ultra-high-voltage transmission lines and the complexity of the corridor environment, the traditional manual inspection method faces serious challenges in terms of efficiency, cost, and safety. In this study, based on power laser inspection technology with a high-precision servo control system, a complete set of laser point cloud processing technology is proposed, covering three core aspects: transmission line extraction, scene recovery, and operation status monitoring. In transmission line extraction, combining the traditional clustering algorithm with the improved PointNet++ deep learning model, a classification accuracy of 92.3% is achieved in complex scenes; in scene recovery, 95.9% and 94.4% of the internal point retention rate of transmission lines and towers, respectively, and a vegetation denoising rate of 7.27% are achieved by RANSAC linear fitting and density filtering algorithms; in the condition monitoring segment, the risk detection of tree obstacles based on KD-Tree acceleration and the arc sag calculation of the hanging chain line model realize centimetre-level accuracy of hidden danger localisation and keep the arc sag error within 5%. Experiments show that this technology significantly improves the automation level and decision-making accuracy of transmission line inspection and provides effective support for intelligent operation and maintenance of the power grid. Full article

This study examines the role of farm forestry Farmer Field Schools (FFSs) in strengthening climate resilience in mixed crop–livestock systems in dryland Kenya. Based on interviews and focus group discussions in Embu and Taita Taveta, this study finds that FFS participation enhanced tree cultivation, market monitoring, and group-based learning, with greater involvement of women in decision-making. While FFS households showed stronger motivation for continued learning and experimentation, it has not consistently translated into statistically significant improvements in climate resilience outcomes as measured by recent drought and disturbance impacts. Limited water access emerged as a major barrier. The findings suggest that while FFSs foster adaptive learning and farm-level innovation, their contribution to climate resilience requires integration with cross-sectoral strategies, especially water management and institutional support. Full article

With the transformation of urban development patterns and profound changes in population structure in China, outdoor activity spaces in residential areas are facing common issues such as obsolete infrastructure, insufficient barrier-free facilities, and intergenerational conflicts, which severely impact residents’ quality of life and hinder high-quality urban development. Guided by the principles of all-age friendly and inclusive design, this study innovatively integrates eye-tracking and multi-modal physiological monitoring technologies to collect both subjective and objective perception data of different age groups regarding outdoor activity spaces in residential areas through human factor experiments and empirical interviews. Machine learning methods are utilized to analyze the data, uncovering the differentiated response mechanisms among diverse groups and clarifying the inclusive characteristics of these spaces. The findings reveal that: (1) Common Demands: All groups prioritize spatial features such as unobstructed views, adequate space, diverse landscapes, proximity accessibility, and smooth pavement surfaces, with similar levels of concern. (2) Differentiated Characteristics: Children place greater emphasis on environmental familiarity and children’s play facilities, while middle-aged and elderly groups show heightened concern for adequate space, efficient parking management, and barrier-free facilities. (3) Technical Validation: Heart Rate Variability (HRV) was identified as the core perception indicator for spatial inclusivity through dimensionality reduction using Self-Organizing Maps (SOM), and the Extra Trees model demonstrated superior performance in spatial inclusivity prediction. By integrating multi-group perception data, standardizing experimental environments, and applying intelligent data mining, this study achieves multi-modal data fusion and in-depth analysis, providing theoretical and methodological support for precisely optimizing outdoor activity spaces in residential areas and advancing the development of all-age friendly communities. Full article

We sought to incorporate a community-based solution with a family health history (FHH) clinical support program (MeTree) integrated into well-patient appointments with the novel partnership of a public health state-level health information exchange (HIE). The Arkansas—Making History pilot project tested informatics compatibility among these systems and the patients’ electronic medical record (EPIC) in a rural clinic in the north central region of the state, having the state HIE as a means for patients to store and share their FHHs across multiple healthcare providers with updates in real time. We monitored for unexpected issues during the pilot and asked for the perspectives of patients and healthcare providers throughout the project to have a clear understanding of how to implement this project on a larger scale. The greatest barrier to project implementation was the inability of the state HIE to host or share the FHH data. We compensated for the lack of systems compatibility and documented valuable information about patient acceptability and usability of the MeTree platform, as well as gleaning important clinical outcome data from those who completed MeTree FHH accounts in an underserved area. Rural patients need additional technological support in the larger scaling of this project, both in available linkages to community clinics with patient-controlled options for how their data is stored and shared and in Internet connectivity and software options available for ease of use. Full article

Groundwater pollution negatively impacts aquatic ecosystems and human health. On the other hand, conservation practices can help reduce groundwater and surface water pollution. Baseflow from agricultural fields can be an important source of nitrate-nitrite (NN) loads in lakes and other surface water bodies. Riparian agroforestry buffers can be an effective barrier between groundwater NN and surface water bodies. The study aimed to determine the effects of agroforestry buffers and widths on groundwater nitrate-nitrite (NN) exports from an agricultural grazing area into a farm lake using flow and solute transport models. The flow and solute models were calibrated and validated for the weather and land use (grazing) conditions observed during the monitoring period, and these conditions were repeated throughout the 10-year projection. The calibration and validation of the flow and solute transport models were satisfactory, yielding determination coefficients R² > 0.95 and Nash-Sutcliffe coefficients > 0.94. The area of study was modeled under four scenarios: tree-only buffers [cottonwood (Populus deltoides Bortr. ex Marsh.)]; grass-only buffers ([Tall fescue Schedonorus phoenix (Scop.) Holub, Red clover (Trifolium pretense L.), and Lespedeza (Lespedeza Michx)]); tree + grass buffers (a combination of the same tree and grass species of the other two scenarios; and a no-buffer scenario. The tree-only, grass-only, and tree + grass buffers reduced the total mass of NN discharged from the study unit to the lake by 98%, 97%, and 99%, respectively, compared to the no-buffer scenario. Doubling the buffer width from 15 m to 30 m decreased the NN discharge to the lake by 16-fold. Moreover, 7.5 m wide buffers had up to nine times greater NN discharge than 15 m buffers. Results show that agroforestry buffers with trees and grasses in riparian areas significantly remove NN exports in groundwater from agricultural fields, protecting the environment and human health. Full article