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Flash floods pose a distinct challenge compared to traditional fluvial flooding, with infrastructure-based solutions proving less effective. Effective responses hinge on advanced early warning systems providing actionable information, emphasising the necessity for computational flood forecasting models. However, hydrodynamic models, renowned for accuracy and completeness, face limitations due to computational intensity. This study explores two 2D flood forecasting models, RIM2D and SERGHEI, both with GPU implementations which allow us to maximise the forecast lead time. While RIM2D is less computationally intensive, suitable for operational use, SERGHEI, with higher computational costs, targets large-scale high-performance computing (HPC) systems. The assessment of applicability and trade-offs is carried out on the 2021 Eifel flood event, particularly in the lower Ahr valley. A set of simulations were performed at various resolutions from 1 to 10 m, which reveal similar accuracy among both models at coarser resolutions, yet discrepancies arise at finer resolutions due to the distinct formulations. Both models exhibit a rapid computational cost escalation, but at resolutions equal to or coarser than 5 m, forecasts are remarkably faster than the real-time ideal for operational use, paving the way for their use in early warning systems. However, higher resolutions necessitate multi-GPU and HPC capabilities, underlining the importance of embracing such technology in addressing broader flood domains.

Abstract We investigate the link between quantum position-verification (QPV) and holography established in [1] using holographic quantum error correcting codes as toy models. By inserting the “temporal” scaling of the AdS metric by hand via the bulk Hamiltonian interaction strength, we recover a toy model with consistent causality structure. This leads to an interesting implication between two topics in quantum information: if position-based verification is secure against attacks with small entanglement then there are new fundamental lower bounds for resources required for one Hamiltonian to simulate another.

Abstract Background Multimorbidity is increasingly prevalent among ageing patients, leading to reduced daily functioning. To address the challenges posed by multimorbidity in older adults, a person- and context-centred approach is needed. This study aimed to develop a questionnaire as a self-assessment tool for older adults focusing on functioning in general practice based on the International Classification of Functioning, Disability and Health (ICF). Methods A mixed-methods approach was employed in the development and validation of the German EFA23 (Erfassung Funktionaler Gesundheit im Alter – 23 Fragen; Assessing Functional Health in Old Age – 23 questions) questionnaire. Based on an ICF subset developed in a preparatory phase and consensus study, questionnaire items were formulated and tested in a qualitative pretest, followed by a validation study. A workshop with general practitioners (GPs) was held to develop a supplementary manual for GPs on how to interpret the questionnaire results and discuss them with the patients. Results A total of 69 items were developed and tested in the qualitative pretest with 15 respondents, resulting in 37 items for the validation study. The validation study, involving 237 older adults, showed the presence of one significant principal component. It demonstrated good internal consistency (Cronbach’s alpha = 0.967) and construct validity, showing positive correlations with established assessment tools. Descriptive statistics showed differences between the means of self-assessment by patients and an external GP assessment. The final EFA23 questionnaire consists of 23 items assessing limitations in functioning. Conclusions The EFA23 questionnaire can be used as a valid self-assessment instrument to measure functioning in older adults, supporting a person- and context-centred approach in general practice.

Abstract This work deals with designing an advanced integral sliding mode controller (AISMC) for residual current compensation (RCC) inverters connected with arc suppression coils to compensate for power distribution networks where the main idea is to alleviate hazardous circumstances caused by electric faults on powerlines. The key advancement in the proposed AISMC over traditional sliding mode controllers is the utilization of an improved exponential reaching law which ensures the faster convergence of the desired control objective that is the fault current compensation in this particular application. An improved exponential reaching law (IERL) used in this work is a combination of the exponential and constant‐proportional reaching laws (while existing approaches use constant reaching laws) which assists to minimize the current injection error through the RCC inverter in the steady‐state. The integral action in conjunction with the exponential function in the sliding surface, based on which the proposed controller is designed, helps to eliminate the chattering effects in a quickest way that can be evidenced from the settling time and percentage overshoot. The feasibility of the proposed AISMC is theoretically assessed by analyzing the stability using the Lyapunov stability theory. Simulation and processor‐in‐loop results further justify the theoretical foundation by confirming the desired current injection and compensating voltage and current due to the fault. Finally, results are compared with a TIMSC for demonstrating the superiority of the AISMC.

Abstract Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability.

Abstract Despite considerable advances in flood forecasting during recent decades, state-of-the-art, operational flood early warning systems (FEWS) need to be equipped with near-real-time inundation and impact forecasts and their associated uncertainties. High-resolution, impact-based flood forecasts provide insightful information for better-informed decisions and tailored emergency actions. Valuable information can now be provided to local authorities for risk-based decision-making by utilising high-resolution lead-time maps and potential impacts to buildings and infrastructures. Here, we demonstrate a comprehensive floodplain inundation hindcast of the 2021 European Summer Flood illustrating these possibilities for better disaster preparedness, offering a 17-hour lead time for informed and advisable actions.

IntroductionPoint-of-care (POC) manufacturing of chimeric antigen receptor (CAR) modified T cell has expanded rapidly over the last decade. In addition to the use of CD19 CAR T cells for hematological diseases, there is a growing interest in targeting a variety of tumor-associated epitopes.MethodsHere, we report the manufacturing and characterization of autologous anti-CD20 CAR T cells from melanoma patients within phase I clinical trial (NCT03893019). Using a second-generation lentiviral vector for the production of the CD20 CAR T cells on the CliniMACS Prodigy®.ResultsWe demonstrated consistency in cell composition and functionality of the products manufactured at two different production sites. The T cell purity was >98.5%, a CD4/CD8 ratio between 2.5 and 5.5 and transduction rate between 34% and 61% on day 12 (harvest). Median expansion rate was 53-fold (range, 42–65-fold) with 1.7-3.8×109 CAR T cells at harvest, a sufficient number for the planned dose escalation steps (1×105/kg, 1×106/kg, 1×107/kg BW). Complementary research of some of the products pointed out that the CAR+ cells expressed mainly central memory T-cell phenotype. All tested CAR T cell products were capable to translate into T cell activation upon engagement of CAR target cells, indicated by the increase in pro-inflammatory cytokine release and by the increase in CAR T cell amplification. Notably, there were some interindividual, cell-intrinsic differences at the level of cytokine release and amplification. CAR-mediated T cell activation depended on the level of CAR cognate antigen.DiscussionIn conclusion, the CliniMACS Prodigy® platform is well suited for decentralized POC manufacturing of anti-CD20 CAR T cells and may be likewise applicable for the rapid and automated manufacturing of CAR T cells directed against other targets.Clinical trial registrationhttps://clinicaltrials.gov/study/NCT03893019?cond=Melanoma&term=NCT03893019&rank=1, identifier NCT03893019.

Abstract Aircraft observations of the four chloromethanes: carbon tetrachloride (CCl4), methyl chloride (CH3Cl), dichloromethane (CH2Cl2), and chloroform (CHCl3), collected over North America between 2000 and 2022, were used to evaluate their vertical distributions and temporal trends in the atmosphere. We examine the vertical profiles, from the surface to the lower stratosphere (LS), of these increasingly important contributors to ozone‐depleting chlorine in both altitude and potential temperature space. Airborne chloromethane trends were compared with those measured at long‐term, ground‐based monitoring stations. Below 20 km altitude, CCl4 trends were decreasing at all levels studied in the North American atmosphere (−1.1 ppt yr−1). CHCl3 and CH2Cl2 airborne observations were comparable to ground network measurements: CHCl3 increased between 2000 and 2018 and then decreased leading to a negligible trend over the 22 years studied and CH2Cl2 has been increasing at all levels in the troposphere (+2.41 ppt yr−1, 2000–2022,

Abstract Bioactive material concepts for targeted therapy have been an important research focus in regenerative medicine for years. The aim of this study was to investigate a proof-of-concept composite structure in the form of a membrane made of natural silk fibroin (SF) and extracellular vesicles (EVs) from gingival fibroblasts. EVs have multiple abilities to act on their target cell and can thus play crucial roles in both physiology and regeneration. This study used pH neutral, degradable SF-based membranes, which have excellent cell- and tissue-specific properties, as the carrier material. The characterization of the vesicles showed a size range between 120 and 180 nm and a high expression of the usual EV markers (e.g. CD9, CD63 and CD81), measured by nanoparticle tracking analysis (NTA) and single-EV flow analysis (IFCM). An initial integration of the EVs into the membrane was analyzed using scanning and transmission electron microscopy (SEM and TEM) and vesicles were successfully detected, even if they were not homogeneously distributed in the membrane. Using direct and indirect tests, the cytocompatibility of the membranes with and without EVs could be proven and showed significant differences compared to the toxic control (p 0.05).

High-resolution flood maps are needed for more effective flood risk assessment and management. Producing these directly with hydrodynamic models is slow and computationally prohibitive at large scales. Here we demonstrate a new algorithm for post-processing low-resolution inundation layers by using high-resolution terrain models to disaggregate or downscale. The new algorithm is roughly 8 times faster than state-of-the-art algorithms and shows a slight improvement in accuracy when evaluated against observations of a recent flood using standard performance metrics. Qualitatively, the algorithm generates more physically coherent flood maps in some hydraulically challenging regions compared to the state of the art. The algorithm developed here is open source and can be applied in conjunction with a low-resolution hydrodynamic model and a high-resolution DEM to rapidly produce high-resolution inundation maps. For example, in our case study with a river reach of 20 km, the proposed algorithm generated a 4 m resolution inundation map from 32 m hydrodynamic model outputs in 33 s compared to a 4 m hydrodynamic model runtime of 34 min. This 60-fold improvement in runtime is associated with a 25 % increase in RMSE when compared against the 4 m hydrodynamic model results and observations of a recent flood. Substituting downscaling into flood risk model chains for high-resolution modelling has the potential to drastically improve the efficiency of inundation map production and increase the lead time of impact-based forecasts, helping more at-risk communities prepare for and mitigate flood damages.

The atmospheric sulfur cycle plays a key role in air quality, climate, and ecosystems, such as pollution, radiative forcing, new particle formation, and acid rain. In this study, we compare the spatially and temporally resolved measurements from the NASA Atmospheric Tomography (ATom) mission with simulations from five AeroCom III models for four sulfur species (dimethyl sulfide (DMS), sulfur dioxide (SO2), particulate methanesulfonate (MSA), and particulate sulfate (SO4)). We focus on remote regions over the Pacific, Atlantic, and Southern oceans from near the surface to ∼ 12 km altitude range covering all four seasons. In general, the differences among model results can be greater than 1 order of magnitude. Comparing with observations, model-simulated SO2 is generally low, whereas SO4 is generally high. Simulated DMS concentrations near the sea surface exceed observed levels by a factor of 5 in most cases, suggesting potential overestimation of DMS emissions in all models. With GEOS model simulations of tagging emission from anthropogenic, biomass burning, volcanic, and oceanic sources, we find that anthropogenic emissions are the dominant source of sulfate aerosol (40 %–60 % of the total amount) in the ATom measurements at almost all altitudes, followed by volcanic emissions (18 %–32 %) and oceanic sources (16 %–32 %). Similar source contributions can also be derived at broad ocean basins and on monthly scales, indicating the representativeness of ATom measurements for global ocean. Our work presents the first assessment of AeroCom sulfur study using ATom measurements, providing directions for improving sulfate simulations, which remain the largest uncertainty in radiative forcing estimates in aerosol climate models.

Proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) is a technique commonly used to measure ambient volatile organic compounds (VOCs) in urban, rural, and remote environments. PTR-ToF-MS is known to produce artifacts from ion fragmentation, which complicates the interpretation and quantification of key atmospheric VOCs. This study evaluates the extent to which fragmentation and other ionization processes impact urban measurements of the PTR-ToF-MS ions typically assigned to isoprene (m/z 69, C5H8H+), acetaldehyde (m/z 45, CH3CHO+), and benzene (m/z 79, C6H6H+). Interferences from fragmentation are identified using gas chromatography (GC) pre-separation, and the impact of these interferences is quantified using ground-based and airborne measurements in a number of US cities, including Las Vegas, Los Angeles, New York City, and Detroit. In urban regions with low biogenic isoprene emissions (e.g., Las Vegas), fragmentation from higher-carbon aldehydes and cycloalkanes emitted from anthropogenic sources may contribute to m/z 69 by as much as 50 % during the day, while the majority of the signal at m/z 69 is attributed to fragmentation during the night. Interferences are a higher fraction of m/z 69 during airborne studies, which likely results from differences in the reactivity between isoprene and the interfering species along with the subsequent changes to the VOC mixture at higher altitudes. For other PTR masses, including m/z 45 and m/z 79, interferences are observed due to fragmentation and O2+ ionization of VOCs typically used in solvents, which are becoming a more important source of anthropogenic VOCs in urban areas. We present methods to correct these interferences, which provide better agreement with GC measurements of isomer-specific molecules. These observations show the utility of deploying GC pre-separation for the interpretation PTR-ToF-MS spectra.

Extensive airborne measurements of non-methane organic gases (NMOGs), methane, nitrogen oxides, reduced nitrogen species, and aerosol emissions from US wild and prescribed fires were conducted during the 2019 NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality campaign (FIREX-AQ). Here, we report the atmospheric enhancement ratios (ERs) and inferred emission factors (EFs) for compounds measured on board the NASA DC-8 research aircraft for nine wildfires and one prescribed fire, which encompass a range of vegetation types. We use photochemical proxies to identify young smoke and reduce the effects of chemical degradation on our emissions calculations. ERs and EFs calculated from FIREX-AQ observations agree within a factor of 2, with values reported from previous laboratory and field studies for more than 80 % of the carbon- and nitrogen-containing species. Wildfire emissions are parameterized based on correlations of the sum of NMOGs with reactive nitrogen oxides (NOy) to modified combustion efficiency (MCE) as well as other chemical signatures indicative of flaming/smoldering combustion, including carbon monoxide (CO), nitrogen dioxide (NO2), and black carbon aerosol. The sum of primary NMOG EFs correlates to MCE with an R2 of 0.68 and a slope of −296 ± 51 g kg−1, consistent with previous studies. The sum of the NMOG mixing ratios correlates well with CO with an R2 of 0.98 and a slope of 137 ± 4 ppbv of NMOGs per parts per million by volume (ppmv) of CO, demonstrating that primary NMOG emissions can be estimated from CO. Individual nitrogen-containing species correlate better with NO2, NOy, and black carbon than with CO. More than half of the NOy in fresh plumes is NO2 with an R2 of 0.95 and a ratio of NO2 to NOy of 0.55 ± 0.05 ppbv ppbv−1, highlighting that fast photochemistry had already occurred in the sampled fire plumes. The ratio of NOy to the sum of NMOGs follows trends observed in laboratory experiments and increases exponentially with MCE, due to increased emission of key nitrogen species and reduced emission of NMOGs at higher MCE during flaming combustion. These parameterizations will provide more accurate boundary conditions for modeling and satellite studies of fire plume chemistry and evolution to predict the downwind formation of secondary pollutants, including ozone and secondary organic aerosol.

IntroductionUrban pluvial flooding is a growing concern worldwide as consequence of rising urban population and climate change induced increases in heavy rainfall. Easy-to-implement and fast simulation tools are needed to cope with this challenge.MethodsThis study describes the development of the parsimonious, GPU-accelerated hydraulic model RIM2D for urban pluvial flood simulations. This is achieved by considering the built-up urban area as flow obstacles, and by introducing capacity-based approaches to consider urban drainage by infiltration on pervious surfaces and sewer drainage from roofs and sealed surfaces. The model performance was analyzed by simulating 8 heavy rainfall events in a test area in the city of Dresden, Germany. For these events detailed discharge measurements of sewer discharge are available, providing a unique dataset for evaluating the sewer drainage simulation, which is of high importance for realistic pluvial inundation simulations in urban areas.Results and discussionWe show that the model simulates the temporal dynamics of the sewer discharge and the sewer volume within acceptable ranges. Moreover, the erratic variation of the simulated to measured sewer discharge suggests that the deviations from the measurements are caused by the precipitation input rather than the model simplifications. We conclude that RIM2D is a valid tool for urban inundation simulation. Its short simulation runtimes allow probabilistic flood risk assessments and operational flood forecasts.

ABSTRACT: There are limited publications about canine subcutaneous mast cell tumors (MCT). International studies have shown that subcutaneous MCT has longer survival times than cutaneous MCT, with lower recurrence and metastasis rates. In addition, subcutaneous MCT has a specific histopathological classification (circumscribed, combined, or infiltrative pattern). Our study evaluated 162 cases of subcutaneous MCT diagnosed from 2014 to 2017 in Brazil. The mean age of the animals was 8.6 years, with a predominance of females and higher incidence in dogs with mixed breed (n=40), followed by Boxer (n=20), Labrador Retriever (n=14), Golden Retriever (n=11) and Pug (n=10). Regarding histopathological characterization, the most common infiltrative pattern represented 54.3% of cases, followed by circumscribed (34.8%) and combined (11%) patterns. The mean mitotic index (MI) was 1.04, with 93.9% of cases presenting MI≤4 and 53.1% MI=0. The data found in this Brazilian study regarding subcutaneous MCT does not differ from those described in American studies, suggesting similar genetic and epidemiological factors. The evaluated proliferation indices suggest that subcutaneous MCT presents slow progression and should be evaluated as a distinct form of cutaneous MCT.

Neste artigo o autor procura mostrar que "ética do discurso" e "ética da libertação" são posições complementares. Recusa a objeção de que a ética discursiva está comprometida com o eurocentrismo e discute a objeção levantada por E. Dussel de que o adversário genuíno do discurso não é o cético mas o cínico.

Rock bursts are dynamic phenomena in underground openings, causing damage to support and infrastructure, and are one of the main natural hazards in underground coal mines. The prediction of rock bursts is important for improving safety in mine openings. The hazard of rock bursts is correlated with seismic activity, but rock bursts are rare compared to non-destructive tremors. The five machine learning classifiers (multilayer perceptron, adaptive boosting, gradient boosting, K-nearest neighbors, and Gaussian naïve Bayes), along with an ensemble hard-voting classifier composed of these classifiers, were used to recognize rock bursts among the dominant non-destructive tremors. Machine learning models were trained and tested on ten sets of randomly selected data obtained from one of the active hard coal mines in the Upper Silesian Coal Basin, Poland. For each of the 627 cases in the database, 15 features representing geological, geomechanical, mining, and technical conditions in the opening as well as tremor energy and correlated peak particle velocity were determined. Geological and geomechanical parameters of the coal seams and surrounding rocks were aggregated into a single GEO index. The share of rock bursts in the database was only about 8.5%; therefore, the ADASYN balancing method, which addresses imbalanced datasets, was used. The ensemble hard-voting classifier most effectively classified rock bursts, with an average recall of 0.74.

With the introduction of the European Commission's “Safe and Sustainable-by-Design” (SSbD) framework, the interest in understanding the implications of safety and sustainability assessments of chemicals, materials, and processes at early-innovation stages has skyrocketed. Our study focuses on the “Safe-by-Design” (SbD) approach from the nanomaterials sector, which predates the SSbD framework.In this assessment, SbD studies have been compiled and categorized into reviews, case studies, and frameworks. Reviews of SbD tools have been further classified as quantitative, qualitative, or toolboxes and repositories. We assessed the SbD case studies and classified them into three categories: safe(r)-by-modeling, safe(r)-by-selection, or safe(r)-by-redesign. This classification enabled us to understand past SbD work and subsequently use it to define future SSbD work so as to avoid confusion and possibilities of “SSbD-washing” (similar to greenwashing). Finally, the preexisting SbD frameworks have been studied and contextualized against the SSbD framework.Several key recommendations for SSbD based on our analysis can be made. Knowledge gained from existing approaches such as SbD, green and sustainable chemistry, and benign-by-design approaches needs to be preserved and effectively transferred to SSbD. Better incorporation of chemical and material functionality into the SSbD framework is required. The concept of lifecycle thinking and the stage-gate innovation model need to be reconciled for SSbD. The development of high-throughput screening models is critical for the operationalization of SSbD. We conclude that the rapid pace of both SbD and SSbD development necessitates a regular mapping of the newly published literature that is relevant to this field.

Flotation stands out as a successful and extensively employed method for separating valuable mineral particles from waste rock. The efficiency of this process is subjected to the distinct physicochemical attributes exhibited by various minerals. However, the complex combination of multiple sub-processes within flotation presents challenges in controlling this mechanism and achieving optimal efficiency. Consequently, there is a growing dependence on machine learning methods in mineral processing research. This paper provides a comprehensive overview of machine learning and artificial intelligence techniques, presenting their potential applications in flotation processes. The review demonstrates advancements discussed in scholarly research over the past decade and highlights a growing interest in utilizing machine learning methods for monitoring and optimizing flotation processes, as demonstrated by the increasing number of studies in this field. Recent trends also suggest that the course of flotation process monitoring, and control will increasingly focus on the refinement and deployment of deep learning networks developed specifically for froth image extraction and analysis.

Considerable variation exists in the literature on published rates of surgical site infection (SSI) after carpal tunnel release, ranging over 20-fold, from 0.28% to 6.4%. The reason for this variability is unknown. A retrospective review was conducted on 748 open carpal tunnel releases performed under wide-awake local anesthetic no tourniquet in an in-office procedure room. The following three different definitions of infection were used for analysis: definition A: prescription of an oral antibiotic; Definition B: SSI definition by the Centers for Disease Control and Prevention; Definition C: infection that required reoperation. Infection rate by definition A was 8.9% (67/748), by definition B was 2.3% (17/748), and by definition C was 0.4% (3/748), resulting in a 22-fold range. The infection rate after carpal tunnel release is heavily influenced by the definition of SSI. The definition of SSI needs to be considered when making comparisons, either in research or quality assurance/quality improvement applications. When analyzing SSI rates, the exact definition of infection must be ascertained to accurately compare an individual's practice or institutional data to the literature. [ABSTRACT FROM AUTHOR]