Waters (United States)

To achieve accurate prediction of tunnel boring machine (TBM) penetration rate (PR) under complex geological conditions, this study proposes an interpretable machine learning (ML) framework optimized using Bayesian optimization (BO). A dataset comprising 411 samples collected from two TBM projects was established, incorporating key geological parameters (uniaxial compressive strength, elastic modulus, and Poisson’s ratio) and operational parameters (thrust force and rotation speed). Seven ML models, including decision tree, Gaussian process regression, k-nearest neighbors, random forest (RF), support vector regression, extreme gradient boosting, and light gradient boosting machine, were systematically optimized using BO under five-fold cross-validation. The results demonstrate that ensemble learning models exhibit superior predictive performance. Among them, the RF model achieved the highest accuracy on the testing set with an R 2 of 0.9582, followed by the light gradient boosting machine with an R 2 of 0.9565. SHapley Additive exPlanations (SHAP) analysis indicates that uniaxial compressive strength is the dominant factor controlling PR, while thrust force act as the primary controllable parameters. The proposed framework effectively integrates high predictive accuracy with strong interpretability, providing a reliable methodological basis for intelligent TBM performance prediction and construction parameter optimization.

The growing integration of intermittent renewables, especially solar PV, challenges grid stability and limits the operational flexibility of conventional hydropower. Beyond hydrological variability, hydropower is constrained by mandatory ecological flow releases, which restrict its ability to reduce generation during high solar output or ramp up during peak demand due to water scarcity. This paper introduces Ecological Flow Energy Storage (EFES), a cost-effective, environmentally sound solution to enhance grid flexibility. EFES involves building small reservoirs downstream of existing hydropower plants to temporarily store ecological flows. This decouples ecological requirements from electricity generation, enabling hydropower plants to halt generation during solar-rich periods without breaching environmental mandates. Stored water can later be released through upstream turbines during low solar availability or peak demand, effectively turning a regulatory constraint into a strategic energy storage asset. EFES supports better integration of renewables, optimizes hydropower dispatch, and enables economic arbitrage, all while preserving downstream ecological services. In Brazil, the estimated EFES potential is 14.3 GW, with a storage cost that can be ten times lower than battery storage. This paper explores the technical feasibility, economic viability, and environmental implications of EFES, highlighting its promise as a scalable, low-cost energy storage solution for modern power systems. • A proposal for increasing the flexibility of hydropower for both ecological and energy services • Downstream micro-reservoirs can decouple hydropower generation from ecological flow. • Enabling hydropeaking to reduce solar curtailment • In Brazil, Ecological Flow Energy Storage can shave ∼14.3 GW peak. • A scalable, low-cost path to integrate VRE via hydropower flexibility

ABSTRACT Extra virgin olive oil (EVOO) is widely recognized for its health‐promoting properties; however, its comprehensive chemical composition remains incompletely characterized. To uncover new constituents, EVOOs from diverse geographical origins were analyzed by ultra‐performance liquid chromatography–high‐definition mass spectrometry (UPLC–HDMS). A total of 340 qualitative features were detected across the EVOO samples. Among these, 125 features—characterized by accurate mass, collision cross‐section (CCS) values, and tandem mass spectrometry (MS/MS) fragmentation patterns—were consistently present in EVOOs from all origins and were therefore considered potential intrinsic compounds of EVOO. From this set, 78 compounds were successfully identified, including 24 known compounds and 54 previously unreported compounds. The remaining 47 features could not be definitively identified. Comparative analysis revealed that EVOO contains a distinct chemical profile relative to sesame and peanut oils. Specifically, 56 compounds were found exclusively in EVOO. Additionally, EVOO exhibited a greater diversity and higher relative abundance of flavonoids and pentacyclic triterpenoids, whereas it contained fewer types and lower levels of fatty acids and fatty acid amides. EVOO is rich in a wide range of novel compounds, many of which may contribute to its documented health‐promoting properties.

Mass spectrometry is a widely used method for the identification of molecules in complex samples. Current tools for database search of experimental spectra against libraries of molecules are not scalable. Moreover, these tools are often limited to known molecules and only perform an exact search. Here, to address this, we introduce Variable Interpretation of Spectrum-Molecule Couples, or VInSMoC, a mass spectral database search algorithm for the identification of variants of molecules. VInSMoC removes some false identifications by estimating the statistical significance of matches between spectra and molecular structures. Benchmarking VInSMoC in a search of 483 million spectra from GNPS against 87 million molecules from PubChem and COCONUT revealed 43,000 known molecules and 85,000 variants that were previously unreported. VInSMoC further facilitates identifying putative microbial biosynthesis pathways of promothiocin B and depsidomycin in Streptomyces bellus and Streptomyces sp. F-2747, respectively.

Open waste burning, large-scale fires, and maritime disasters produce partially burnt plastic called ‘pyroplastic’. Chemical markers would provide a complementary method to appearance and physical properties for identifying pyroplastics in environmental samples, particularly with respect to microplastics. Pyroplastic can contain significant quantities and unique distributions of parent polycyclic aromatic hydrocarbons (PAHs) with molecular weights up to 278 Da. Because of this enrichment, we considered whether large PAHs (≥ 24 ringed-carbons) could serve as chemical markers for pyroplastics. To address this, we developed a high-temperature gas chromatography atmospheric pressure chemical ionization (GC-APCI) method, targeting large PAHs with molecular weights ranging from 314–424 Da, using tandem mass spectrometry (MS/MS). Method development was performed using National Institute of Standards and Technology standard reference materials (SRMs) previously characterized for PAHs greater than 302 Da. A PAH class-specific MS/MS acquisition scheme combined with a simple, generic microextraction provided sensitive and specific detection without the need for sample fractionation or cleanup. Pyroplastics collected during the 2021 M/V X-Press Pearl ship fire and plastic spill were analyzed. Semi-quantitative comparison shows that the pyroplastic samples contain over two orders of magnitude greater amounts of the total 16 large PAHs (314–424 Da) than were found in unburnt plastic pellets, reflecting trends previously observed for parent PAHs up to 278 Da. Qualitative comparison of samples and SRMs revealed multiple potential candidates (including 1,3,5-triphenylbenzene) suitable for further study as markers of pyroplastics in complex environmental samples. A suite of chemical markers for pyroplastics should prove helpful in monitoring efforts for air quality, waste management, microplastic pollution, and fires at the forest-urban interface.

Abstract Background Antipsychotic drugs (APDs) are frequently used in combination with other drugs, leading to potential pharmacokinetic and pharmacodynamic drug interactions. Many of these interactions have not been adequately studied in clinical research. This study presents a simple, cost-effective and robust LC-MS method which is based on protein precipitation for the simultaneous analysis of 35 APDs in human plasma. Methods In brief, 50 µL of plasma and 100 µL of protein precipitation solvent, a 70:30 (v:v) mixture of MeOH:0.1M ZnSO4(aq) containing stable isotope labeled internal standards was added to a microfuge tube. The samples were vortex mixed briefly and centrifuged at 18,000 g for five minutes at room temperature. Subsequently, 100 µL of the supernatant was diluted with 100 µL distilled water and the samples were mixed on a shaker for three minutes at 850 rpm prior to analysis. Samples were injected onto a Waters XSelect™ HSS C18 SB XP Column using an ACQUITY™ UPLC™ I-Class PLUS FL System and eluted using a water/methanol/formic acid/ ammonium acetate gradient. The 35 APDs were quantified using a Xevo™ TQ-S micro Mass Spectrometer operated in electrospray positive mode with multiple reaction monitoring. Results For each analyte, the method was demonstrated to be linear across calibration range, with analytical sensitivity investigations showing precise quantification (<20% CV, =15% bias). Non-linearity at all levels for all compounds was less than 10% across the targeted linearity interval. Coefficients of variation (CV) for total precision and repeatability for low, mid and high QCs were =8.7% CV and =7.4%, respectively (n = 25). Recoveries for the low and high QC across all analytes ranged from 91.5-108.7% and 85.7-112.7%, respectively. Mean extraction efficiencies for the 35 APDs ranged from 52.2%-96.9%. Conclusion A fast (less than 5 minutes per injection) and inexpensive LC-MS/MS clinical research method was developed for 35 APDs, based on protein precipitation and requiring only 50µL of plasma. The method demonstrated good recovery and extraction efficiency for each analyte, with minimal matrix effects, and total precision =8.7% CV. For Research Use Only. Not for Use in Diagnostic Procedures. ACQUITY, UPLC, XSelect and Xevo are trademarks of Waters Technologies Corporation.

Aging in mice is tracked by a lysosomal "clock", where glycerophosphodiesters and cystine - metabolites causally linked to juvenile lysosomal storage disorders - gradually accumulate in lysosomes of the brain, heart, skeletal muscle and adipose tissue.