Advancing Open Science

Civil Air Patrol (CAP) imagery, often the first post-disaster observation, is designed for 2D interpretation rather than multi-temporal volumetric measurement. We build post-storm digital surface and terrain models from uncontrolled CAP imagery with structure-from-motion in western North Carolina’s open Swannanoa Valley and forested Hickory Nut Gorge. Surfaces are co-registered to pre-event LiDAR using roads as pseudo ground control, corrected for terrain-correlated bias, and thresholded with a spatially variable, calibrated Level of Detection. Before bias correction, bare-earth surfaces match withheld post-event LiDAR on stable terrain to 0.68 and 0.82 m normalized median absolute deviation. Volumes are 2017-to-2024 change, lower bounds in coverage, and conditional on the bias model, residual screen, and detection window. Corrected valley surfaces record 125,000 m³ of erosion and 123,000 m³ of deposition, and a sampled image check dates about 13% of that change to the sortie. Under the same limit, the uncorrected surface reports five times the deposition, and the alternative bias model gives gross terms 3.6 to 4.2 times larger. The gorge at Chimney Rock recorded 76,000 m³ of erosion and 84,000 m³ of positive change, just under half dating to the sortie, including land deposition partly cleared before the post-event LiDAR flew. CAP-derived surfaces can capture transient storage that later surveys cannot.

Background: Type 1 diabetes mellitus (T1DM) is an increasingly common chronic disease caused by autoimmune damage to pancreatic beta cells. Metabolomic profiling may provide insights into metabolic disturbances associated with T1DM and identify candidate biomarkers. Methods: The study included 29 children with T1DM and 11 controls. Targeted serum metabolomic profiling was performed using the AbsoluteIDQ^® p180 kit based on liquid chromatography–mass spectrometry. Differential metabolites were identified using statistical analysis with false discovery rate correction. Receiver operating characteristic (ROC) analysis was applied to evaluate the discriminatory performance of selected metabolites. Results: Significant metabolic alterations were observed in children with T1DM, particularly in glycerophospholipids, acylcarnitines, and selected amino acids. Among glycerophospholipids, 23 metabolites were significantly decreased, whereas one metabolite was increased in the T1DM group. Four acylcarnitines differed significantly between groups, with higher concentrations of octadecenoylcarnitine (C18:1), acetylcarnitine, and malonylcarnitine and/or hydroxybutyrylcarnitine [C3-DC (C4-OH)], and reduced free carnitine. In addition, three amino acids (alanine, arginine, and proline) were significantly decreased, while taurine was increased in T1DM patients. ROC analysis showed high apparent discriminatory performance for C18:1 (AUC = 0.925) and diacyl phosphatidylcholine (PC aa) C42:5 (AUC = 0.922) in this exploratory dataset. These AUC values were obtained from the same dataset used for metabolite discovery and therefore represent apparent in-sample discriminatory performance rather than an externally validated diagnostic performance. Correlation analysis showed that higher HbA1c levels were associated with lower concentrations of several amino acids and lipids; BMI z-score and disease duration were associated mainly with phosphatidylcholines and amino acids. However, none of these correlation-based associations remained significant after FDR correction and should therefore be considered hypothesis-generating. Exploratory multivariable analyses suggested associations of HbA1c, BMI z-score, and disease duration with selected metabolites; however, given the limited sample size and multiple model testing, these findings should be considered hypothesis-generating. Conclusions: Targeted metabolomic profiling revealed significant disturbances in lipid and amino acid metabolism in children with T1DM. In particular, increased C18:1 and decreased PC aa C42:5 may represent candidate metabolic features for distinguishing T1DM patients from non-diabetic individuals. Due to the exploratory study and limited sample size, our results should be interpreted with caution, and further studies in larger, independent cohorts are required to validate these findings.

Alkaline lacustrine environments, due to their unique coupling mechanism of sedimentation, diagenesis, and hydrocarbon generation, have a significant influence on the generation and migration efficiencies of shale oil. However, the microscopic mechanism by which alkaline fluids regulate the primary migration of hydrocarbons remains unclear. Taking the shale of the Lucaogou Formation in the Junggar Basin as an example, this study integrates extraction experiments, total organic carbon (TOC) determination, gas chromatography–mass spectrometry (GC-MS) analysis, and molecular dynamics simulation to reveal the influence of saline–alkaline solutions on the total amount of hydrocarbons expelled from shale, as well as to systematically investigate the controlling mechanism of saline–alkaline media on the selective expulsion of hydrocarbon molecules from shale. The results show that compared with pure water extraction, shale TOC decreased by 6.78% and 15.00% after salt and alkali extraction, respectively, indicating that salt and alkali can promote hydrocarbon expulsion. The nC₂₀₋/nC₂₀₊ values of n-alkanes extracted from pure water, salt, and alkali were 0.42, 2.34, and 2.30, respectively, indicating that salt alkali has different effects on the excretion of large- and small-molecule alkanes, with small-molecule alkanes being more sensitive. Molecular simulation further confirms that weakly adsorbed nC₁₅ molecules are more prone to desorption after adding alkali, while nC₂₃ adsorption is almost unaffected, indicating that alkali can promote hydrocarbon expulsion by affecting hydrocarbon adsorption. This mechanism provides a new theoretical explanation for the oil expulsion efficiency of high-quality source rocks and the enrichment patterns of shale oil in alkaline lacustrine environments, and it has important guiding significance for the selection of shale oil exploration targets and the optimization of fracturing methods.

Background: Personalized medicine is a new milestone in surgical disciplines. Orthopedic surgery is no exception. Opportunities and solutions to previously unsolvable problems have become possible thanks to the advent of customized implants. Therefore, it is incredibly important to accumulate and share long-term experience in using these implants, without fear of revealing and admitting mistakes or pointing out ways to prevent and solve them. Clinical case: The presented clinical case demonstrated stable implant fixation for 10 years. This clinical case describes the stages of developing an understanding of the use and effectiveness of 3D-printed implants in orthopedic surgery. Key errors and inaccuracies in the management of the patient that led to complications were analyzed, and solutions were proposed and successfully implemented. Conclusions: Custom 3D-printed implants are an effective and promising method for reconstructing massive acetabular defects. The authors believe that their experience will serve as a good example for orthopedic surgeons in choosing the right solutions for the management of patients with massive bone defects.

This paper presents the Sloth–Vine Optimization Algorithm (SVOA), a dual-population method combining elite-guided differential search, vine exploration, opposition trials, and conditional role exchange. The SVOA implementation is evaluated on 12 CEC2022 functions at dimensions 10 and 20, using 30 prescribed seeds and counted function–evaluation budgets. Its average mean fitness ranks are 1.333 and 2.083, respectively; at dimension 20, the overall rank is tied with ABC. Its strongest consistent relative performance occurs on the three hybrid functions, while some competing methods perform better elsewhere. Engineering experiments distinguish a path-planning application variant from the unchanged core used for heterogeneous unmanned surface vehicle (USV) allocation. On one fixed obstacle map, the path variant obtains 30/30 feasible simplified paths at 3000 evaluations per run, with a mean pre-simplification fitness value of 225.31. For 43 tasks and 11 USVs, the unchanged core obtains feasible allocations in 5/30 runs at 250,500 evaluations, with a best feasible makespan of 11.5 h. The other 25 failures remain in the statistics. Ten-seed paired penalty tests preserve path feasibility on this map but show limited USV success across all settings. These results support problem-dependent performance, without establishing universal superiority or reliable feasible USV allocation.

Shipboard gateways send many sensor channels to shore subscribers with different needs over narrow, intermittent links, yet how Message Queuing Telemetry Transport (MQTT) topic structure, broker placement, Quality of Service (QoS), publishing rate, and outage recovery should be combined has not been evaluated on real shipboard data. Traces from five liquefied natural gas carriers were replayed through four topic structures on a mock broker, on amqtt, Mosquitto, and EMQX, across two hosts, and over emulated satellite-like links, with repeated runs. Per-sensor topics cost fewer bytes per useful sample than vessel batches for subscribers needing under 44–48 % of the channels, and an analytical model predicts this break-even from the channel inventory within 0.8 %. Latency tails arose whenever the offered message rate exceeded the broker delivery rate and vanished at matched rates for every QoS level. Brokers differed in queuing or discarding under overload, so latency should be reported with the delivery ratio. Onboard bridge filtering cut forwarded bytes to 6–35 % and, on a 512 kbit/s link, 95th-percentile latency from 12 s to 0.9 s. Event-first recovery delivered events fastest only below a capacity-bounded token rate. The results give researchers a predictive cost model and operators computable gateway settings.

Liquefied natural gas (LNG) is increasingly central to global energy strategy, positioned as a lower-emission transition fuel. That positioning assumes fugitive methane emissions across the LNG supply chain are well-characterised, yet direct measurement campaigns at LNG facilities have found operator-reported inventories capturing only 59–83% of actual emissions—a gap that legacy emission factors derived from upstream U.S. oil and gas facilities are poorly equipped to resolve. As measurement-based accountability replaces estimation factors in voluntary frameworks and emerging regulation, the question is which technologies are fit for purpose. This review provides a comprehensive, cross-scale assessment of methane-sensing technologies—from component-level leak detection through to satellite surveillance—evaluated against the physical and operational realities of LNG facilities. Trade-offs in sensitivity, spatial and temporal resolution, environmental dependence, and regulatory readiness are assessed to determine where each performs reliably and where it falls short. While technology is mature at the component level, validation data specific to LNG environments is scarce across all scales, and no single platform provides a complete solution. The most consequential gaps are methodological: the absence of standardised validation protocols, inadequate regulatory harmonisation, and a persistent lag between instrument capability and accepted practice. Six recommendations are proposed: standardised field validation protocols, hybrid monitoring frameworks, regulatory pathways that accommodate emerging technologies, measurement infrastructure in LNG-intensive regions, data transparency with top-down/bottom-up reconciliation, and improved monitoring coverage for intermittent and difficult-to-access fugitive emissions. Together, these define a roadmap for closing the gap between current practice and the accountability that credible LNG reporting demands.

Phishing detectors trained on public benchmarks score highly in-domain yet miss scams aimed at university users. We audit TF-IDF, MiniLM and RoBERTa detectors with 5146 scams published by five U.S. universities, 594 scams posted since 2023, 2684 legitimate university announcements and mailing-list messages, and 17,880 labelled job advertisements. In one benchmark, message year separates phishing from legitimate mail; in both, donor-identifying terms, 2% of the vocabulary, carry 14–16% of the legitimacy weight. Trained on that benchmark, TF-IDF and RoBERTa detect scams containing academic vocabulary about 15 percentage points less often. Thresholds set for a 5% false-positive rate on benchmark mail flag between 1.0% and 87.8% of legitimate job advertisements. Calibrated instead on job advertisements grouped by company, TF-IDF flags 2.1% of university announcements but 65.3% of short legitimate financial messages, and appended legitimate-looking words cut its recall from 0.828 to 0.123. Bounding legitimacy evidence yields closed-form certificates for whitespace-delimited appends, prepends and interior insertions. A budget-split ensemble certifies 0.259 recall against appends or prepends of any length at 0.706 clean recall, whereas adversarially trained TF-IDF falls to 0.078 under a 40-token white-box append. Monotone components, however, flag many long formal letters.