Active width is a key indicator of bedload dynamics in gravel-bed rivers. While theoretical models typically refer to instantaneous bedload active width, its direct measurement in the field remains difficult due to high spatio-temporal variability. Consequently, morphological active width, derived from detectable bed changes after flood events, is often used as a proxy, though its relationship to bedload transport dynamics across different morphologies, flood magnitude, and survey frequencies remains poorly understood. This study aims to (a) quantify active width in relation to dimensionless stream power and morphology; (b) assess differences between bedload and morphological active widths across time intervals; and (c) evaluate the Exner timescale framework for linking transport dynamics to spatio-temporal morphological evolution. A physical model replicating braided, wandering, and alternate bar morphologies under controlled flow conditions, combining time-lapse imagery and DEM differencing, was used to address these objectives. Results were compared with field data from the Tagliamento (Italy), Rees (New Zealand Aotearoa), and Sunwapta (Canada) rivers. Findings show a strong correlation between active width and dimensionless stream power and reveal a large effect of the timespan, ranging from 0.4 to 3 times the Exner timescale. Morphological active width was consistently lower than the time-integrated bedload active width, with larger differences for single-thread/wandering morphologies, reaching 33%, compared to braiding (about 10%) and remaining constant with increasing timespan. Comparison with repeated DEMs of Difference (DoDs) in the field confirmed the validity of the approach, with both dimensionless stream power and the timespan between surveys controlling the morphological active width. • Flume experiments map bedload and morphological change dynamics at high resolution. • Instantaneous bedload active width correlates with dimensionless stream power. • Braided rivers show higher lateral mobility than other systems at similar timescale. • In braided rivers, morphological width proxies bedload width at short timescale. • The Exner timescale links bedload transport and flood properties across conditions.

The increase in utility-scale PV generation and the displacement of synchronous machines reduce system strength, reactive power headroom, voltage resilience, and overall power system stability, motivating a robust comparison of various mitigation technologies beyond static load-flow or PV assessments. RMS time-domain simulations are performed for balanced and unbalanced contingencies, and performance is quantified using post-fault voltage dip depth, undervoltage area (V < 0.9 pu.), recovery time to nominal, and RoCoF. These metrics are aggregated into a single weighted composite severity score, which is then normalised to the baseline to form the dynamic voltage resilience index (DVRI) and the Frequency Disturbance Relative Index (FDRI). The results show that the converter-based reactive power support devices deliver the fastest and most controllable post-fault voltage restoration, with the STATCOM achieving the lowest composite penalty and best DVRI under severe fault conditions but the poorest FDRI during PV plant trip/reconnection events. The synchronous condenser (SC) improves post-fault recovery through excitation driven reactive capability and increased short-circuit contribution, but its recovery to nominal voltage levels is slower and can produce negative-sequence current under unbalanced fault conditions whilst producing the smallest frequency disturbance and best FDRI. The SVC provides effective steady-state regulation but becomes less effective during extremely low voltages due to the voltage-dependent reactive power output, and its FDRI remains close to baseline. The BESS-GFM is dependent on the inverter current limits and the control priorities, which influence both voltage recovery and response times, achieving an FDRI scoring second to the SC. These metrics are combined into baseline normalised composite indices (DVRI and FDRI) using explicitly dimensionless sub-metrics (dip magnitude, exposure area, and recovery delay for voltage and deviation magnitude, windowed RoCoF, and exposure for frequency). Equal weights are used as a neutral baseline, and a weight sensitivity study is included to confirm that technology rankings are robust to plausible variations in weighting choice.

Abstract. Catchment classification supports regionalisation and runoff prediction in data limited regions by organising basins into hydrologically coherent classes. China spans strong gradients in moisture availability, temperature regime, snow influence, and terrain, yet discharge observations remain sparse. We develop and evaluate an integrated climate-landscape classification for 13 487 HydroBASINS catchments using a hierarchical self-organizing map and fuzzy c-means (SOM–FCM) framework. Six hydroclimatic indices delineate climate regions on a 0.25° grid, and catchments are classified within each region using geomorphological and drainage network descriptors. The framework yields six climate regions and 35 classes, with fuzzy memberships characterising transitional areas. Hydrological relevance is assessed using seasonal hydrographs and event scale flow duration curves (FDCs) for ten gauged headwater catchments, and 13 flow signatures for 722 headwater basins matched to a discharge reanalysis product. Seasonal regimes are organised mainly by climate regions, whereas event response and high flow behaviour are modulated by landscape classes. Flow magnitude and high flow frequency signatures discriminate classes most strongly, while duration metrics show weaker contrasts. The resulting typology provides a transferable basis for selecting donor basins and constraining model parameters, thereby improving runoff prediction and regionalisation in ungauged catchments across China.

Abstract To enhance the wide-band measurement performance of magnetohydrodynamic angular rate sensor (MHD ARS) in precision optoelectronic tracking systems, accuracy fusion with an inertial measurement unit (IMU) is essential. This paper presents a Kalman filter with magnitude–frequency response attenuation ratio (MAR)-enhanced adaptive process noise covariance, which effectively improves the signal-to-noise ratio (SNR) and reduces scale factor degradation in the fused output caused by differences in sensor frequency responses. First, this paper proposes a frequency band discrimination method based on MAR, which iteratively calculates the MAR parameter from the sensor measurements to determine the signal's frequency band. Second, the process noise covariance is adaptively adjusted based on the MAR discrimination result to optimize the Kalman gain. Finally, the proposed algorithm is implemented on an embedded platform, and attitude is resolved, achieving high-precision measurement across the full frequency band. The fusion performance was validated through angular velocity, angular velocity dynamic tracking, and angular position response experiments, demonstrating significant improvements in tracking accuracy and SNR within the tested bandwidth. In the 0.1–300 Hz angular position test, the fused output exhibited a maximum amplitude ratio fluctuation below 0.04 and a maximum phase difference fluctuation below 13° relative to a precision laser reference. This study provides a novel sensor fusion solution for wide-band high-precision line-of-sight (LOS) stabilization in optoelectronic tracking systems on highly dynamic platforms under vibration environments.

This paper provides a feasibility study of a non-invasive microwave-based glucose-sensing system based on a small printed slot antenna with etched step-impedance resonators (SIRs) on an FR4 substrate in the ground plane at approximately 5.7 GHz. The sensor proposed takes advantage of the effect of the antenna resonant frequency and reflection coefficient (S11) perturbation due to the dielectric loading of a human finger placed in the antenna near field. Instead of declaring direct glucose specificity, this paper is dedicated to understand whether the measures of RF can be translated to the invasive glucose values under the condition of controlled positioning. A vector network analyzer was used to measure the experimental values where resonant frequency and S11 magnitude were obtained at the point of peak sensitivity due to fixed finger placement at the point. These RF properties were associated with invasively measured glucose values using three modeling methods: a simple analytical linear formula, a second-degree Polynomial Ridge regression model, and a Random Forest machine learning model. The comparative analysis has established that nonlinear data-driven models outperform the analytical formulations significantly with the highest predictive accuracy being the Random Forest model (R2 = 0.72, RMSE = 10.57 mg/dL, MAE = 5.16 mg/dL). The findings affirm that the impacts of antenna loading control the raw measurements, but the trend related to glucose can be extracted upon machine learning calibration under controlled conditions. The research provides a methodological framework of RF-based non-invasive glucose sensing and the need to employ various phantom-based validation, sub-subject-based modeling, or clinically based evaluation metrics in future studies.

The integration of shape-memory alloy (SMA) wires into composite laminates offers a promising approach for active vibration damping. Towards this goal, this study investigates the damping behavior of hybrid random mat E-glass/epoxy composite plates with embedded SMA wires under electrically active and inactive conditions. The composites are tested using a Laser Doppler Vibrometer (LDV) and an impact hammer to assess the effect of SMA wire activation on the natural frequencies and vibration behavior of composites. For a fixed number of active SMA wires, differences in vibration behavior are evaluated between outer- and inner-wire activation configurations in both two-ply and four-ply composite plates. The results show that SMA wire activation significantly affects damping behavior, while the mode shapes remain unchanged. The magnitude and frequency of the first natural frequency as well as the quality factor (Q-factor) decrease in composites with activated SMA wires compared to the inactive configuration, indicating enhanced energy dissipation. Under the fully active condition, a reduction in vibrational amplitude of approximately 42–60% and a frequency shift of approximately 10–17% are observed. Compared to outer-wire activation, inner-wire activation results in greater reductions in vibration magnitude, reaching approximately 7–13%.

• Annual framework combing downscaled WRF, EnergyPlus and RTD detection • Integrated design increases summer comfort hours >60% under future climates • Integrated measures cut cooling load >70% and winter discomfort hours about 30% • Reduces RTD days by 44% and mean RTD magnitude around 20% by late century • Sobol GSA identifies shading, buffer zones and thermal zoning as high-leverage This paper develops a year-round, policy-ready framework that evaluates and optimises thermal resilience in subtropical low-income housing by explicitly incorporating rapid temperature-drop (RTD) events and novel cross-season performance metrics. The study employs WRF-downscaled hourly climates (historical, mid- and late-century SSPs), year-round EnergyPlus simulations, a sliding-window RTD detector and Sobol global sensitivity analysis to compare three design strategies (baseline, summer-optimised, fully integrated annual). Six annual metrics are introduced: May–Sep comfort hours (operative T ≤ 28°C), summer cooling-load reduction, Nov–March cold-discomfort hours (T < 18°C), winter heating-load change, RTD frequency (≥ 9°C drop / 6 h) and mean RTD magnitude. Results show integrated passive-adaptive interventions preserve summer cooling benefits while substantially improving winter resilience: large increases in summer comfort hours, >70% cooling savings in favourable cases, ∼30% fewer winter cold-discomfort hours, a 44% reduction in RTD days and meaningful reductions in RTD magnitude under late-century extremes. The framework exposes seasonal trade-offs (shading vs. winter solar gain; ventilation vs. airtightness) and yields ranked, high-leverage design levers for codes and retrofit policy. Framing design using annual RTD-aware metrics prevents single-season solutions that inadvertently shift risk between summer and winter.

Accurate estimation of earthquake source parameters—such as moment magnitudes, corner frequencies, and stress drops—is essential for improving seismic hazard assessments and understanding earthquake physics. In this study, moment magnitudes (MW) are calculated for 31,581 earthquakes associated with wastewater injection in the Raton Basin (located along the border between northern New Mexico and southern Colorado) between 2016 and 2024 using radiative transfer theory to fit coda decay envelopes. Our results show that it is feasible to estimate moment magnitudes down to MW ~1 with coda envelopes from a small local monitoring network. Significant differences were found between MW and local magnitudes (ML) for small earthquakes (M &lt; 3.0). A linear relationship was optimized to convert ML to MW: MW = 0.7ML + 0.96 and MW = 0.73 ML + 0.99 (for the events reported by the U.S. Geological Survey), which can be applied in future studies of Raton Basin seismicity. We find that b-values calculated employing different methods and using ML are approximately 1.0, while those using MW range from 1.2 to 1.4. A larger estimate of the b-value could influence interpretations of the statistical behavior of earthquakes associated with injection and consequently seismic hazard assessments based on a magnitude–frequency distribution. The potential differences between local versus moment magnitude-based earthquake statistics should be considered in other seismically active regions.

Abstract The durability of an airborne ultrasonic sound source was evaluated based on the material’s fatigue characteristics represented by the stress–number of cycles to failure curve. A 55 kHz aluminum sound source was driven under controlled vibrational stress until failure. The results showed a clear correlation between vibrational stress and fatigue life, with failure occurring above approximately 165 MPa. Measurements of admittance magnitude–frequency characteristics and vibration displacement distribution revealed deterioration in resonance characteristics after failure. These findings demonstrate that the proposed method is effective for quantitatively evaluating and predicting the lifetime of sound sources.

ABSTRACT Seismicity analysis and earthquake hazard assessment require catalogs of independent events and accurate estimates of completeness. The magnitude of completeness (Mc) defines the threshold below which earthquakes are likely to be missed; declustering, used to remove dependent events, can substantially alter Mc by reshaping the lower tail of the frequency–magnitude distribution. Because such changes propagate to the Gutenberg–Richter b-value, method choices may bias downstream analyses. Using a regional instrumental catalog from southern Italy, we try to quantify how widely used declustering approaches affect catalog-based estimates of Mc. We apply three declustering families: fixed window (Gardner and Knopoff original formulation, including Gruenthal and Uhrhammer windows), linked window (Reasenberg, three parameter configurations), and nearest neighbor. Then we estimate Mc with five catalog-based methods: maximum curvature, goodness-of-fit test, median-based analysis of segment slope, entire magnitude range, and magnitude of completeness by b-value stability. Comparing across declustered and observed catalogs, we evaluate how declustering event removal modifies the frequency–magnitude distribution and in turn the sensitivity of catalog-based Mc methods to these modifications. Resampling is used to assess precision and stability, the results remain essentially unchanged even with small bootstrap sizes, indicating the need but large numbers of resamples are unnecessary. Overall, the analysis highlights that declustering choice and completeness estimation are critical decisions and should be reported jointly when deriving the b-value for seismic hazard applications.

CNC machines are used in production industries for batch production. In CNC machining, a minor issue can cause production downtime, reducing productivity and profit for the Industry. In CNC machines, drilling machine maintenance is crucial because of the complexity of the drill tools. Drill tools have complex shapes and geometries, making tool wear prediction particularly challenging. Tool wear in CNC drilling severely hinders performance and affects the dimensional accuracy and surface finish obtained. This paper presents a machine-learning-based approach to drill wear detection using the Hilbert–Huang Transform for feature extraction from airborne Acoustic Emission (AE) signal and the CatBoost algorithm for classification. For controlled drilling operations, AE signals from four wear-condition samples representing Healthy Tool (HT), Low Wear (LW), Medium Wear (MW), and Severe Wear (SW) were recorded. Wear levels of 0.3mm,0.6mm, and 0.9mm for the drill bits of 3.0 mm, 3.2 mm, 3.4 mm, 3.6 mm, and 3.8 mm diameters were created using Electrochemical Machining in the Lab. Using AE sensors, the signals were collected and converted into the required format with the support of signal conditioning and a data acquisition system. LabVIEW software was used to display the signal, and it was then decomposed using the Hilbert-Huang Transform (HHT) to obtain the required Intrinsic Mode Functions (IMFs). Features needed for classification, such as magnitude, entropy, and instantaneous frequency, were selected in the time-frequency domain. These features were used as input to a classifier (CatBoost), which was trained and evaluated using 10-fold cross-validation. HHT-CatBoost achieved 99.1% accuracy, indicating a promising sign for the proposed algorithm in real-time maintenance for small- to medium-sized datasets.

ABSTRACT Surface-based temperature inversions (SBIs) strongly influence air temperature variability in high-latitude mountains, yet their spatial structure remains poorly resolved. To address this gap, a dense network of air temperature sensors was deployed and elevational transect analysis (ETA) was applied to quantify surface lapse rates (SLRs) and SBI characteristics at fine temporal and spatial scales. SBI frequency and SLR magnitude increased significantly in anomalously warm summers, linking large-scale climate variability and valley-scale elevational temperature patterns. Contrary to the common assumption of linear lapse rates, annual and monthly mean SLRs were most strongly positive within the lowest 60 m of the valleys and weakened rapidly upslope. This highlights the importance of sampling valley bottoms and lower slopes to capture SBI intensity. Using ETA, SBI depth was quantified for the first time and found on average to be shallow (<250 m), rarely extending above the ridgetops (500 m). SBI development and breakup were not solely driven by cold air pooling and daytime convective mixing but were also driven by processes such as warming or cooling aloft. These findings provide new insights into the SBI structure in subarctic valleys. They strengthen the physical basis for representing temperature variability, essential to modeling surface phenomena such as permafrost distribution.

Achieving non-invasive and high-fidelity electrophysiological recording, particularly electroencephalography (EEG), on dynamic and irregular human skin remains a central challenge in soft bioelectronics, as materials rarely reconcile liquid-like adaptability with solid-like stability. Here, we overcome this limitation by designing a viscoelastic ionogel governed by a dynamic enthalpy-entropy balance. Salt-bridge hydrogen bonds form a low-entropy and high-interaction network, intrinsically limiting the capacity for entropic energy storage. This network then self-organizes with a soft phase into a bicontinuous nanostructure. Acting as a mechanical parallel circuit, this architecture introduces a broad molecular relaxation spectrum, providing broadband enthalpic dissipation and realizing broadband enthalpy-entropy compensation. Consequently, the ionogel exhibits a frequency-independent viscoelastic plateau (G'≈G'') spanning over nine orders of magnitude in frequency (10-4 to 105Hz) and a wide temperature range (-30°C to 40°C). The ionogel reduces skin-electrode impedance by more than an order of magnitude compared to commercial electrodes and maintains high-fidelity electrophysiological recordings during 72-h continuous wear. Integrated with a deep learning framework, it enables high-precision decoding of EEG signals, achieving 95% accuracy in classifying eight distinct emotional states. This work establishes a generalizable thermodynamic design principle for soft bioelectronic interfaces, offering broad potential for neural diagnostics, emotional monitoring, and wearable neuroprosthetics.

One of the main issues in the pipes’ structure that could be affected by wind that led to the failure of the cylinder pipe structure is vortex-induced vibration (VIV). Therefore, there is a need to control the wind that is going through the pipe to avoid vibration. This phenomenon leads to the failure of the structure due to the resonance phenomenon when the natural frequency of the structure is equal to the vortex frequency. The main contributions of this work are mathematical modeling of the system using NARX and suppressing the vibration caused by wind currents through simulation and experiment ways. PI-PSO control employed to reduce the unwanted vibration as a simulation work. Then, an experimental study implemented on the structure as an open and closed loop control techniques to decrease the vibration with disturbance vibrations of 5 and 10 m/s. Open loop active vibration control (OLAVC) is proposed in this work using dual control rods made from hollow stainless steel and driven by dual DC motors in two positions at 6, 8, 10, and 12 DCV. The control rods are located beside the main cylinder pipe (CRBCP) and from the upper and lower of the hollow cylinder pipe. The effectiveness of the passive control strategy was confirmed before supplying electricity to the two DC motors on both sides. The PI controller tuned by the PSO method was developed to control unwanted model vibration. Based on the control results, the best values of K P and K I were 35.78 and 50 respectively at the lowest MSE of 1.3557×10 -4 , and the frequency magnitude was reduced by 81.17 %. The findings also showed that the cylinder pipe vibration could not be sufficiently suppressed by the passive control method. While OLAVC succeeded in reducing the vibration when the motor voltage was at 12 V. Finally, the closed-loop control technique decreased the vibration up to 61.24 % and 58.65% for disturbance wind speeds of 5 and 10 m/s, respectively.

Retropubic midurethral slings (RP-MUS) are placed utilizing external anatomic landmarks and tactile sensation as the trocar creates a pathway from vaginal entry-dissection to suprapubic exit-site. Training a novice in virtual reality (VR) to achieve expert-level surgical skills can avoid the need for models, cadavers, or practicing on patients, but it relies on the existence of clear and replicable expert-level pathways. The objective of this study was to determine whether surgical motion and haptic feedback from experts can clearly distinguish performances from novice learners. We tracked the motion and haptic feedback for novice medical students and board-certified urogynecologists as they performed RP-MUS surgery in VR. We measured differences using change of acceleration (jerk), similarity scores, and the number of clusters per participant group. Using a t -test, we examined differences between skill levels (expert vs. novice) in mean, magnitude, and jerk frequency scores. Eight experts and 16 novices participated in the study. Surgical pathways were less variable in experts versus novices, as indicated by fewer clusters for experts than novices (hand motion: 4 vs 18 clusters, head motion: 3 vs 17, haptic force: 6 vs 25). Overall, maximum motion-distance was also higher for novices than experts. Experts had more deliberate, efficient, smoother paths. T -tests found significant differences between experts and novices on time to complete the surgical simulation and jerk scores (hand motion and haptic pressure applied). Across all measures, experts produced fewer sudden movements, demonstrated more consistent and purposeful motion, and completed tasks more efficiently. Although some metrics-such as cluster mean jerk and axis coordination-did not always reach statistical significance, they showed very large effect sizes favoring expert performance. Taken together, these results underscore that experts consistently outperform novices across both quantitative and qualitative assessments of procedural motion. Motion metrics can thus provide feedback for surgical training.

Social housing of animals is well recognized as a fundamental element in providing for the well-being of social animals during laboratory research activities but is contraindicated in some experimental designs. To ascertain if social housing represented a potential confounder in studies determining influenza A virus (IAV) pathogenicity conducted in the ferret model, we analyzed data from ferrets inoculated with 56 distinct IAV strains, where animals were housed either singly or in pairs postinoculation. Parameters examined included frequency of lethal outcomes, timing and magnitude of clinical signs (weight loss and fever), and magnitude and kinetics of viral shedding in the upper respiratory tract. Statistical differences between IAV-inoculated ferrets housed in either setting were not consistently detected among any parameters examined, supporting that data from ferrets inoculated with a diverse range of avian- and mammalian-origin IAV housed singly or in social pairs may be combined and analyzed without confounding based on this variable. This study supports the utility of performing retrospective analyses to ensure results obtained from in vivo experimentation are interpreted to the highest standard possible. Further, it aligns with ethical obligations of researchers who perform in vivo experimentation to ensure these studies are designed to meet both animal welfare considerations and research goals.

Humoral and cellular immunogenicity of COVID-19 vaccine boosters in participants with advanced HIV disease.

Icing and environmental variations pose major challenges for structural health monitoring (SHM), as they can alter sensor responses in ways that resemble structural damage. Reliable damage identification therefore requires understanding how temperature, surface water, ice accretion, and operational loads influence SHM signals. This work investigates the environmental sensitivity of two SHM techniques, Electromechanical Impedance (EMI) and Guided Waves (GW), using piezoelectric transducers integrated on an aluminium plate and on a carbon-fibre reinforced polymer (CFRP) rotor blade. In the first campaign, controlled static tests were carried out on an aluminium plate inside a climate chamber, with temperatures varied from 20 °C to –40 °C under dry and iced conditions. EMI measurements showed systematic temperature-dependent shifts of impedance magnitude and resonance frequencies, while ice introduced additional spectral features linked to changes in boundary conditions. GW results revealed strong amplitude attenuation with decreasing temperature and further reduction under ice, whereas time-of-flight remained largely unchanged. To extend the investigation to realistic operating conditions, GW measurements were performed on a CFRP rotor blade in a spinning climate chamber. These measurements were conducted as a secondary task within a larger icing campaign, requiring the SHM system to be adapted to existing constraints and resulting in increased noise. Despite this, two robust GW features, peak amplitude and total signal power, were extracted across several operating states including water impingement, rotation up to 750 rpm, dynamic icing, and static iced conditions. Both features showed consistent qualitative sensitivity to temperature, rotation, water, and ice accretion. Overall, the combined experiments demonstrate that EMI and GW signals are significantly influenced by environmental and operational conditions, often to an extent comparable to structural damage. These findings emphasize the need for environmental compensation strategies to ensure reliable EMI- and GW-based damage detection and localization on aerostructures, and provide a foundation for future studies involving controlled damage scenarios.

To evaluate the impact of a new deep-learning image reconstruction (DLR) algorithm on image quality and potential dose reduction compared with a hybrid iterative reconstruction (IR) algorithm under chest CT conditions. Acquisitions were performed on an image quality phantom at 5 CTDIvol (0.4/2.5/5.0/7.5/9.5 mGy). Raw data were reconstructed using soft tissue and lung kernels at Level 5 among the 9 levels available on the IR algorithm (IR-5) and the 5 levels on the DLR algorithm (from D1 to D5). The noise power spectrum (NPS) and task-based transfer function (TTF) were computed. The detectability index (d') was computed to model subsolid pulmonary nodule and high-contrast pulmonary nodule for lung images and low-contrast soft tissue mediastinal nodule for soft tissue images. For both kernels, noise magnitude and average NPS spatial frequencies decreased from D1 to D5 and were lower than those obtained with IR-5. For all inserts studied and both kernels, TTF values at 50% decreased from D1 to D5 and were lower than those obtained with IR-5. For all three simulated lesions and both kernels, d' values increased from D1 to D5 and were higher than those obtained with K5. Compared to IR-5, a dose reduction potential was found for each DLR level. Compared to IR-5, noise magnitude and detectability values were better, but noise texture and spatial resolution values were degraded, and this degradation increased as the DLR level increased. The results obtained with this new DLR algorithm open up new possibilities for improving image quality and offer significant potential for radiation dose reduction.

Abstract The dark ages 21 cm signal is a promising probe of the currently unobserved infant Universe between the formation of the cosmic microwave background around z ≈ 1100 and the first galaxies around z ≈ 30. A detection of the signal will help researchers understand the nature of dark matter and dark energy, the expansion of the Universe, and any extensions to the concordance ΛCDM model that could explain the reported cosmic dawn 21 cm signal from EDGES and the Hubble tension. In this Letter we take existing constraints on the ΛCDM cosmological model from two early time probes, Planck and the Wilkinson Microwave Anisotropy Probe, and two late time probes, Dark Energy Survey galaxy lensing and clustering and baryon acoustic oscillations, and propagate these through to constraints on the magnitude of the dark ages 21 cm signal. We constrain the magnitude and central frequency of the signal while methodically accounting for uncertainties in the cosmological parameters. We find that within the context of our modeling assumptions and the ΛCDM paradigm, the depth of the dark ages 21 cm signal is known to better than 1 mK and the central frequency to within 0.05 MHz.