Search Results (83,438)

Forest and landscape restoration (FLR) represents a critical nexus of climate change mitigation, biodiversity conservation, and sustainable development. Despite substantial federal investments and commitments, empirical subnational research quantifying the relationships between governance structures, funding mechanisms, and restoration outcomes remains scarce, and integrated implementation frameworks bridging institutional, technical, and socio-economic dimensions are largely absent from the literature. This study presents a mixed-methods analysis of FLR implementation gaps across Maryland, Virginia, and West Virginia. Three Mid-Atlantic Appalachian states selected for their contrasting ecological conditions, governance structures, and restoration trajectories that collectively represent the heterogeneity of subnational restoration challenges. We examined 147 restoration projects (2019–2024), conducted 25 stakeholder interviews, and analyzed federal funding allocations ($428 million) through spatial and temporal frameworks. Our findings reveal five critical implementation barriers: (1) policy incoherence across federal–state–local jurisdictions creating 34% project delays; (2) chronic underfunding with 63% of projects receiving less than 60% of planned budgets; (3) technical capacity deficits affecting 71% of rural communities; (4) inadequate stakeholder engagement mechanisms reducing project sustainability by 45%; and (5) insufficient monitoring frameworks limiting adaptive management. We introduce an Integrated Restoration Implementation Framework (IRIF) that uniquely integrates policy coordination, sustainable financing, technical capacity building, and community engagement within a unified adaptive management cycle, operationalized through empirically derived thresholds, to guide evidence-based interventions. Quantitative analyses demonstrate that multi-stakeholder governance models increase restoration success rates by 2.3-fold (p < 0.001), while integrated funding mechanisms improve long-term sustainability by 67%. Theoretically, this study advances socio-ecological systems scholarship by providing empirical evidence that multi-scalar governance configurations and integrated stakeholder engagement mechanisms are principal determinants of restoration success, advancing the evidence base for adaptive governance approaches in complex federal systems. Our findings provide actionable intelligence for policymakers and practitioners, while underscoring that sustainable FLR in complex federal systems depends on coherent multi-level governance architectures coordinating institutional mandates, financial resources, technical capacity, and community agency across jurisdictional scales. Full article

Enclosed learning spaces, e.g., classrooms, are used in most schools. Open learning spaces, which enable teaching more than one group of students at a time, have become increasingly popular. A recent survey showed that acoustic satisfaction was lower among teachers working in open learning spaces. Our purpose was to compare the acoustic conditions of these learning space types. We investigated the room acoustic quality of 73 learning spaces in 20 schools. Ten schools involved only enclosed and ten both open and enclosed learning spaces. Measurements concerned speech transmission index, STI, background noise level, L_Aeq, and reverberation time, T. Variation in results in both learning space types was rather large. In enclosed learning spaces, STI varied within 0.64–0.83, L_Aeq within 25–47 dB, and T within 0.34–0.82 s. The corresponding variations in open learning spaces were 0.47–0.91, 29–44 dB, and 0.44–0.72 s. The differences between enclosed and open learning spaces were surprisingly small. Due to the different intended uses of these space types, Finnish target values are tighter for open than for enclosed learning spaces. These target values were fulfilled in 56% of enclosed and 9% of open learning spaces. The more frequent violation of target values in open learning spaces was due to the STI being too large at longer distances. Our study provides suggestive evidence that the room acoustic conditions are worse in open than enclosed learning spaces. Further research is needed to prove whether room acoustic conditions could explain worse acoustic satisfaction in teachers. Full article

Accurate runoff forecasting is essential for effective water resource management, hydropower operation, and flood risk mitigation. In this study, daily inflow runoff in the Xin’an River Basin, eastern China, was simulated using four ensemble learning models: Gradient Boosting Decision Tree (GBDT), XGBoost, CatBoost, and Stacking. Among them, the CatBoost model achieved the best performance, with a correlation coefficient (CC) exceeding 0.97, Nash–Sutcliffe efficiency (NSE) above 0.95, and reduced RMSE and MAE compared with the currently operational hydrological model. To extend the forecast lead times, two hydro–meteorological coupled models were developed by integrating the CatBoost model with a single numerical weather prediction model (EC) and a dynamically weighted multi-model ensemble precipitation forecast system (OCF). The coupled models were evaluated for lead times up to 240 h. The forecast skill value was highest within 96 h, with CC values above 0.80 and NSE around 0.50. The OCF-coupled model demonstrated improved reliability for lead times of 48–96 h, whereas the EC-driven forecasts performed better within the first 48 h. Case studies during the 2021–2022 flood seasons confirmed that the coupled framework accurately reproduced flood evolution and peak discharge dynamics, demonstrating its practical value for medium-range runoff forecasting in humid river basins. Full article

Morus macroura ‘Panzhihua No. 1’ is a dual-purpose cultivar valued for its edible leaves and fruits. Derived from an ancient mulberry tree, it is a unique local germplasm resource. During asexual propagation, M. macroura exhibits sexual variation closely associated with fruit and leaf yield. To explore the molecular mechanisms of sexual dimorphism and its effects on nutritional traits, we integrated transcriptomic and metabolomic analyses of male and female inflorescences and leaves. Key sex-biased genes were enriched in plant hormone signaling, flavonoid biosynthesis, and carbohydrate metabolism pathways. Female plants had elevated expression of ethylene-responsive transcription factor 1 (ERF1), EIN3-binding F-box proteins (EBF1/2), and Chalcone synthase (CHS) genes and higher levels of bioactive flavonoids, including isoquercitrin and kaempferol derivatives. In contrast, male plants had increased expression of gibberellin 20-oxidase (GA20ox) and DELLA genes and accumulated glycosides, which are beneficial for leaf development. These findings reveal how sex-linked metabolic networks shape mulberry tissue functional profiles, providing molecular targets for breeding. Full article

Reverse parking maneuvering of a vehicle with a trailer system is a difficult task to complete for human drivers due to the multi-body nature of the system and the unintuitive controls required to orientate the trailer properly. The problem is complicated with the presence of other vehicles that the trailer and its connected vehicle must avoid during the reverse parking maneuver. While path-planning methods in reverse motion for vehicles with trailers exist, there is a lack of results that also offer collision avoidance as part of the algorithm. This paper hence proposes a modified Hybrid A*-based algorithm that can accommodate the vehicle–trailer system as well as collision avoidance considerations with the other vehicles and obstacles in the parking environment. One of the novelties of this proposed approach is its adaptability to the vehicle with trailer system, where limits of usable steering input that prevent the occurrence of jackknife incidents vary with respect to system configuration. The other contribution is the addition of the collision avoidance functionality which the standard Hybrid A* algorithm lacks. The method is developed and presented first, followed by simulation case studies to demonstrate the efficacy of the proposed approach. Full article

This study proposes an end-to-end mathematical framework to automatically transform warehouse layout images into optimization-ready route matrices. The objective is to convert visual spatial information into a discrete, graph-based representation suitable for combinatorial route optimization. The problem is formulated as a mapping from continuous image space to a structured grid representation, integrating image segmentation, graph construction, and Traveling Salesman Problem (TSP)-based routing. Synthetic warehouse layouts were generated to create labeled training data, and a U-Net convolutional neural network was trained to perform multi-class segmentation of warehouse elements. The predicted grid representation was then converted into a graph structure, where feasible cells define vertices and adjacency defines edges. Shortest path distances were computed using Breadth-First Search, and the resulting distance matrix was used to solve a TSP instance. The segmentation model achieved approximately 98% training accuracy and 95–97% validation accuracy. The generated route matrices enabled successful construction of feasible and optimal round-trip routes in all tested scenarios. The proposed framework demonstrates that warehouse layouts can be automatically transformed into discrete mathematical representations suitable for logistics optimization, reducing manual preprocessing and enabling scalable integration into digital logistics systems. Full article

Bio-CO₂ is part of the natural carbon cycle and represents a sustainable carbon source for the production of Renewable Fuels of Non-Biological Origin (RFNBOs), such as synthetic methanol. This study addresses the critical knowledge gap in aligning diverse biogenic CO₂ sources with e-methanol requirements in the EU by providing harmonized mapping, based on datasets, literature sources, and reported industrial statistics at the sectoral and country level. Bio-CO₂ streams from biogas and biogas upgrading, biomass combustion, pulp and paper, bioethanol production, and the food and beverage sector are evaluated for total emissions, CO₂ concentrations and purity, the geographical distribution, seasonality, and impurity profiles. Results show that approximately 350 Mtpa of bio-CO₂ are emitted across the EU, with highly heterogeneous characteristics. Biogas upgrading and fermentation-based processes generate highly pure CO₂ streams (>98–99%), yet their small and dispersed nature complicates logistics. In contrast, biomass-combustion and pulp and paper sectors provide large volumes (around 214.6–298.2 Mtpa and 73.9 Mtpa CO₂, respectively), but in diluted streams (typically 3–15% and 10–20%). Replacing just 10% of the EU maritime fuel demand with e-methanol would require 53.6 Mtpa of bio-CO₂ and 58 GW of electrolyzer capacity, a stark contrast to the current operational 385 MW. The findings highlight the need for infrastructure planning and aggregation hubs to enable the large-scale deployment of RFNBO methanol in the maritime sector. Full article

As manufacturing progresses, the demand for precision inspection of complex parts has intensified. To guarantee functionality and sensory performance, high-efficiency 3D shape measurement is required. In this paper, a collaborative robot-based approach for efficient and high-precision 3D shape inspection of complex parts is proposed. The system employs a collaborative robot to drive the scanner along optimized trajectories. First, the configuration of the inspection system is presented, and the ideal measurement mode for the sensor is analyzed. Subsequently, adaptive viewpoints are generated through parametric discretization based on surface geometric features. For inter-region scanning path planning, the problem is modeled as the Shortest Path Problem (SPP) within the framework of the Traveling Salesman Problem (TSP) and solved by constructing a Successive Approximation Algorithm (SAA). Furthermore, a Modified Denavit-Hartenberg (MDH) method is applied to establish the precise kinematic model of the collaborative robot. Inverse kinematics solutions are derived to convert planned viewpoints into target joint configurations, thereby achieving precise end-effector pose control. Simulation and experimental results on an engine cover and a cylinder head demonstrate that the proposed approach enables comprehensive 3D shape inspection of complex parts in a single setup and achieves higher efficiency and accuracy compared to existing methods. This work offers a viable solution for integrating robotic actuation and active sensing in the automated inspection of complex geometries. Full article

In this paper, the authors developed a data-driven model to classify radar measurements into precipitation (P) and non-precipitation (NP) echoes using the Random Forest machine learning algorithm. Dual-polarimetric radar variables and their local variability exhibit distinctive characteristics between P and NP echoes. The authors found that using larger search window sizes generally improves classification accuracy, though it involves a trade-off: while it helps eliminate small clusters of NP echoes, it may also suppress weak precipitation signals near storm edges. Incorporating multiscale local variability estimates computed with varying window sizes further enhances classification performance by capturing spatial-scale-dependent features characteristic of P and NP echoes. The main model uses radar variables obtained from a single scan and demonstrates consistent performance across all distances from the radar. This consistency allows reliable use of the model out to 230 km—the maximum range at which dual-polarimetric variables are used for rainfall estimation from NEXRAD radars—without significant degradation in accuracy due to range effects. Supplementing the model with independent information from GOES-16 infrared channel products further improves classification by helping to eliminate localized NP echoes remaining after the main model, particularly those caused by wind turbines that mimic precipitation in dual-polarimetric signatures. This is based on the tendency of water vapor and/or raindrops to absorb terrestrial radiation, thereby lowering brightness temperatures. A practical challenge remains near the radar, where the sampling volume is small and signal processing (e.g., sidelobe impact and ground clutter suppression) can distort radar measurements. The under-detection of precipitation in these regions is likely due to such corrupted data. This issue may be mitigated by adopting a hybrid scan strategy—such as a Constant Altitude Plan Position Indicator (CAPPI)—specifically for regions close to the radar. Full article

Artificial intelligence (AI) has become the leading technology for digital transformation in various industries. However, the digitalization of construction project management (e.g., cost, time, and safety) in the context of AI technology implementation is still limited. Therefore, this paper aims to conduct a systematic literature review of AI technologies in construction project cost, time, and safety management, and identify mainstream application areas, cross-domain synthesis, challenges, research gaps, and future research directions. By adopting the PRISMA approach, a systematic literature review was conducted to retrieve 392 articles from the Scopus database. The results presented mainstream application areas of construction project cost (i.e., cost estimation, cost prediction, cost index forecasting, cost control, cost optimization), time (i.e., planning and scheduling, delay risk prediction, time optimization, cycle time prediction), and safety (i.e., workers’ safety monitoring, on-site safety monitoring, personal protective equipment (PPE) detection, safety report text analysis, fall risk monitoring, safety accident prediction, and safety hazard identification and risk assessment). Moreover, the cross-domain synthesis, challenges, and research gaps of AI technologies in construction project management were discussed. Based on these findings, this paper suggests future directions to extend research in this domain. This paper would contribute to the construction project management research domain by providing key application areas and useful research directions, thus promoting digital transformation in the sector. Full article

Architecture, as a profession, discipline and practice, has played a vital role in designing, constructing and maintaining modern culture. The creative work of imagining and building places, infrastructure and dwellings for the complex activities of contemporary life has contributed to the global world we now inhabit. There are, however, indications that this edifice of modernity is cracking because of external and internal forces that undermine our global society. Climate change, species extinction, and worldwide threats to democracy and governance, along with new technologies, converge and reveal the uncomfortable possibility that modern industrial global culture and civilization may collapse. As a response, an expanding body of ‘stories of collapse’ has emerged to interpret causes, processes, and scenarios. This essay engages with key voices (Rees, Bendell, Lewis, Hagens, de Oliveira, and Macy), to describe in what ways architecture is complicit in this moment, and suggests what ethical and place-based responsibilities may be required of architects and placemakers as collapse unfolds. Full article

This study examines the potential contribution of Solar Electric Vehicles (SEVs) and Vehicle-Integrated Photovoltaics (VIPV) to disaster-related energy resilience through a probabilistic modeling framework. While previous research has highlighted the technical feasibility of EV-based support for microgrids and emergency facilities, it has paid limited attention to the behavioral uncertainty surrounding voluntary energy sharing by EV owners. To address this gap, we develop a Monte Carlo simulation model that integrates technical constraints, solar-generation variability, and heterogeneous participation probabilities to evaluate whether SEVs can sustain essential loads during prolonged outages. The analysis focuses on a worst-case scenario in which external lifelines are disrupted for seven days. Results indicate that approximately 450–1000 SEVs within a 5 km radius are required to maintain a continuous power supply, with BEVs requiring roughly twice as many units due to the absence of onboard PV generation. The findings highlight the sensitivity of resilience outcomes to user behavior and spatial vehicle distribution, underscoring the need for incentive mechanisms to encourage participation. Key limitations include simplified behavioral assumptions, region-specific irradiance conditions, and the exclusion of mobility constraints. Overall, the study provides a quantitative foundation for integrating SEVs into resilience planning while emphasizing the importance of social dynamics in determining real-world feasibility. Full article

Background: Proton beam therapy (PBT) is a valuable alternative to photon radiotherapy of CNS tumors in children and adolescents. While most recent studies deal with the outcome or long-term side effects of PBT, the aim of this study was to investigate the feasibility of PBT with a particular focus on the acute toxicity of a simultaneous radiochemotherapy (sPBCT). Patients and methods: We enrolled 199 children [median age 7.4 years (range, 0.9–17.9)], who received altogether 200 courses of PBT/sPBCT at initial diagnosis (n = 121) or at relapse (n = 79) with sPBCT in 52 (26%) courses. Data collection to PBT/sPBCT was based on the medical records and the KiProReg (Registry study of Standard Proton Therapy in Children at West German Proton Therapy Center) with a primarily descriptive-statistical and logistic regression analysis. Results: During PBT/sPBCT a total of n = 704 adverse events (AEs, mean 3.4 per course) were observed. Eighty-seven of them were graded as high-grade adverse events (HGAEs, Common Terminology Criteria for Adverse Eventº ≥3 (CTCAE)) which occurred in 67 (33.5%) PBT/sPBCT courses. HGAEs were in particular hematotoxicity (n = 43; 64.1%) and infections (n = 18; 26.8%). A significantly higher rate of HGAEs was documented in patients treated with sPBCT (n = 33/52; 63.5%) compared to those with PBT only (n = 34/148; 23.0%) (p = 0.001). In children with sPBCT, 15 (28.8%) patients could not receive the recommended dose or schedule of the planned chemotherapy (CTx) due to HGAEs, with the rate of planned CTx courses performed being significantly lower in patients receiving intensive intravenous CTx (p < 0.001). Interruptions of PBT and of simultaneous CTx were both significantly associated with the occurrence of infections [Odds ratios 3.002 (95% CI 1.005–8.971, p = 0.049) and 3.905 (95% CI 1.005–15.174, p = 0.049)]. Total discontinuation of treatment did not occur. Conclusions: Concurrent CTx during proton therapy is associated with a significant increased risk for HGAE occurrence and therapy interruptions requiring individual dose and schedule adjustments dependent on CTx intensity, very experienced interdisciplinary teams as well as intensive care and in-/out-patient oncology facilities on site. Full article

Titanium dioxide (TiO₂) nanotube arrays (NTAs) were constructed on Ti foil to immobilize Cu₂ZnSnS₄-TiO₂ (CZTS-T/NTAs) via the sol–gel dip-coating technique. The films were characterized by X-ray diffraction (XRD) patterns, field-emission scanning electron microscope–energy dispersive spectroscopy (FESEM-EDX), ultraviolet–visible diffuse reflectance spectra (UV–Vis/DRS), and electrochemical impedance spectroscopy (EIS) techniques. The photocatalytic property of CZTS-T/NTAs was evaluated by the photodegradation of Basic Blue 41 under visible light irradiation. We show that CZTS-T/NTAs have an energy band gap of 2.23 eV, which leads to excellent potential trapping or facilitates the transition of charge carriers under visible light. The parameters R₀ and C₀ of the experimental EIS data, by fitting the proposed electrical circuit, were also discussed. Decreasing R₀ led to an increase in cell capacitance, which resulted in increased carrier generation at the interface between the catalyst and solution and thus an increased photodegradation yield. The response surface methodology (RSM) and central composite rotatable design (CCRD) were used to optimize the effects of the experimental parameters in the degradation process by four key variables (pH, dye concentration, irradiation time, and hydrogen peroxide (H₂O₂) concentration). As a result, the optimized conditions attained a considerable degradation of 95.25%. We also proposed the possible photodegradation mechanism of the photocatalyst. Notably, the proposed catalyst after six consecutive reuse runs retained activity. Full article

Hybrid railway assets such as workshops and depots combine building, mechanical, electrical and plumbing (MEP)/industrial, and linear infrastructure domains, increasing coordination complexity and challenging continuity from the Project Information Model (PIM) to the Asset Information Model (AIM). Although Employer’s Information Requirements (EIR), Asset Information Requirements (AIR), and the BIM Execution Plan (BEP) prescribe deliverables and processes, a persistent gap remains between documentary prescriptions and the auditable evidence needed to support traceable decisions within the Common Data Environment (CDE). This paper proposes an ISO 19650-aligned governance framework that operationalizes the EIR/AIR → BEP → CDE transition by: (i) structuring the asset using Functional Units (FUs) as a stable anchor for PIM → AIM continuity; and (ii) implementing a pre-Published Quality Gate that separates control into three non-substitutable dimensions (spatial, semantic, and data). The approach is implemented as a tool-neutral, reproducible workflow (inputs → checks → outputs → publish) and produces a minimal, persistent evidence package in the CDE (file-level report, package summary, publish/hold decision record, and Nonconformity Report (NCR)/BIM Collaboration Format (BCF) traceability), with explicit roles governing the Shared→ Published transition. Across 22 Industry Foundation Classes (IFC), deliverables from two depot cases and multiple delivery states, All Gates Pass ranged from 25.0% to 44.4% depending on Case × State; overall, 14/22 deliverables (63.6%) would be held pending correction under the gate. Although validated on Spanish railway depots, the framework is grounded in ISO/openBIM standards and is designed for transferability to other international contexts and complex asset types where multidisciplinary federation and PIM → AIM continuity pose similar challenges. Full article