Level Of Precision Research Articles

The GAPS Programme at TNG. LXV. Precise density measurement of TOI-1430 b, a young planet with an evaporating atmosphere

Small-sized ($<4$ $R_ oplus $) exoplanets in tight orbits around young stars (10--1000 Myr) give us the opportunity to investigate the mechanisms that led to their formation, the evolution of their physical and orbital properties, and, in particular, their atmospheres. Thanks to the all-sky survey carried out by the spacecraft, many of these exoplanets have been discovered, and have subsequently been characterized with dedicated follow-up observations. In the context of a collaboration among the Global Architecture of Planetary Systems (GAPS) team, the TESS-Keck Survey (TKS) team, and the California Planet Search (CPS) team, we measured ---with a high level of precision--- the mass and the radius of TOI-1430 b, a young ($ 700$ Myr) exoplanet with an escaping He atmosphere orbiting the K-dwarf star HD 235088 (TOI-1430). By adopting appropriate stellar parameters, which were measured in this work, we were able to simultaneously model the signals due to strong stellar activity and the transiting planet TOI-1430 b in both photometric and spectroscopic series. This allowed us to measure both the radius and mass (and consequently the density) of the planet with high precision, and to reconstruct the evolution of its atmosphere. TOI-1430 is an active K-dwarf star born $700 150$ Myr ago, with a rotation period of rot 12$ days. This star hosts a mini-Neptune, whose orbital period is b 0.000004$ days. Thanks to long-term photometric and spectroscopic monitoring of this target performed with TESS, HARPS-N, HIRES, and APF, we estimate a radius of P,b oplus $, a mass of $M_ P,b oplus $, and thus a planetary density of $ b oplus $. TOI-1430 b is therefore a low-density mini-Neptune with an extended atmosphere, and is at the edge of the radius gap. Because this planet is known to have an evaporating atmosphere of He, we reconstructed its atmospheric history. Our analysis supports the scenario in which, shortly after its birth, TOI-1430 b was super-puffy, with a radius $5 and a mass $1.5 the values of today; in $ 200$ Myr from now, TOI-1430 b should lose its envelope, showing its Earth-size core. We also looked for signals from a second planet in the spectroscopic and photometric series, without detecting any.

Harnessing adapted capsule networks for accurate lumpy skin disease diagnosis in cattle

Lumpy skin disease (LSD) poses a substantial risk to livestock, emphasizing the critical need for reliable diagnostic tools to ensure timely intervention. The considerable economic impact of LSD further accentuates the imperative for efficient diagnosis. In this context, artificial intelligence (AI) emerges as a transformative solution, playing a pivotal role in providing swift detection capabilities. Rapid identification of LSD not only alleviates economic burdens but also impedes the disease’s propagation with in herds. A ground breaking in iterative involves the implementation of an adapted capsule network (CapsNet) expressly designed for diagnosing LSD. This innovative strategy is finely tuned to discern intricate patterns in disease manifestation, achieving an impressive accuracy rateof 97.6%. The model’s effectiveness is evident in it is capacity to differentiate between infected and healthy cases, with precision, recall, and F1 score metrics registering at 9.65%, 97.1%, and 96.3%, respectively. This high level of precision underscores the model’s proficiency in minimizing errors, solidifying its role as a dependable tool for precise LSD diagnosis and intervention, contributing significantly to the overall health and economic well-being of livestock populations.

EKLT: Kolmogorov-Arnold attention-driven LSTM with Transformer model for river water level prediction

Water level prediction is crucial for water resource management and flood warnings. Hybrid LSTM-based methods are widely used but face the following challenges: (1) The attention mechanism based on the universal approximation theorem (UAT) is the mainstream method for improving performance, but its essence is infinite approximation of functions, and the accuracy is difficult to improve; (2) LSTM struggles with modeling complex, long-term dependencies. To address these problems, we apply empirical mode decomposition (EMD) and propose input-spatial attention based on the Kolmogorov-Arnold theorem (KAT) for precise water level feature representation. Cascaded LSTM and Transformer structures capture long-term dependencies. Finally, temporal attention is embedded to achieve accurate water level prediction. Experiments show the proposed method achieves RMSE, MAE, MAPE and R2 values of 0.1870, 0.1328, 1.1228 and 0.9540 in the Liaohe River of Liaoning Province, China, and 0.3027, 0.1844, 4.6034 and 0.8659 in the Hunhe River, respectively, indicating its strong predictive performance.

Low frequency quartz tuning fork as hydrogen sensor

The use of hydrogen as a sustainable energy source is pushing the development of innovative sensing strategies for the monitoring of H2 concentration during its production processes and transportation. In this work, we propose a custom quartz tuning fork (QTF) as sensor for the detection of high concentrations of hydrogen in air. The selectivity derives directly from values of molar mass and the viscosity of hydrogen molecules, significantly different from those of the other main constituents of air. Exciting the fundamental flexural mode of a commercially available 12.4 kHz-QTF, we demonstrated the linear dependence of its resonance frequency and quality factor on the H2 concentration, passing through their relationship with molar mass and the viscosity of the H2-air mixture. Monitoring the shift of the resonance frequency of the QTF, the H2-component in air can be estimated with a precision level of 0.74% and accuracy error of 1.82%. The same parameters resulted 0.40% and 4.29%, respectively, when Q values are evaluated. Finally, a beat-frequency approach was proposed to speed up the acquisition in few seconds, monitoring both resonance parameters of the QTF.

People reward others based on their willingness to exert effort

Individual contributors to a collaborative task are often rewarded for going above and beyond—salespeople earn commissions, athletes earn performance bonuses, and companies award special parking spots to their employee of the month. How do we decide when to reward collaborators, and are these decisions closely aligned with how responsible they were for the outcome of a collaboration? In Experiments 1a and 1b (N=360), we tested how participants give bonuses, using stimuli and an experiment design that has previously been used to elicit responsibility judgments (Xiang et al., 2023a). Past work has found that responsibility judgments are driven both by how much effort people actually contributed and how much they could have contributed (Xiang et al., 2023a). In contrast, here we found that participants allocated bonuses based only on how much effort agents actually contributed. In Experiments 2a and 2b (N=358), we introduced agents who were instructed to exert a particular level of effort; participants still rewarded effort, but their rewards were more sensitive to the precise level of effort exerted when the agents decided how much effort to exert. Together, these findings suggest that people reward collaborators based on their willingness to exert effort, and point to a difference between decisions about how to assign responsibility to collaborators and how to incentivize them. One possible explanation for this difference is that responsibility judgments may reflect causal inference about past collaborations, whereas providing incentives may motivate collaborators to keep exerting effort in the future. Our work sheds light on the cognitive capacities that underlie collaboration.

A novel mathematical control algorithm (HNU Shading) for different window shades based on HNU solar model

Window shading control helps maximize daylight use for creating comfortable indoor luminous environments and reduces over-illuminance and glare discomfort. This study proposes a novel mathematical algorithm (the HNU Shading algorithm) for calculating the distribution of direct sunlight indoors and adjusting window shades to avoid over-illuminance and glare indoors. The algorithm uses the sun position, window-ambient illuminance, window design parameters, and the controlled area (desktop and occupant’s head) as inputs to control different window shades (the vertical blind, horizontal blind, exterior awning, and interior shutter in this study), and the performance was verified via experiments and simulations. The results show that the HNU Shading algorithm has a high precision level of predicting the distribution area of direct sunlight with the error at 0.01–0.07 m. And it prevents the high illuminance from the desktop and keeps it lower than 3,000 lx under most of conditions with different window directions, shade types, seasons, and sunlight intensities. Meanwhile, with the HNU Shading algorithm, the Daylight Glare Probability (DGP) is lower than 0.4 during the period when intolerable glare happens. This study contributes to configuring practical and universal real-time window shading controls for different buildings with different window shade types, such as offices, classrooms, libraries, airports, and railway stations.

Modeling and analysis of internal excitation and vibration characteristics of gears with early-stage crack and 3D tooth surface error

Affected by the tooth surface errors, the vibration characteristics of early crack failures are usually various under different tooth surface precision level. Therefore, to reveal the influence of three-dimensional (3D) tooth surface error (TSE) on the internal excitation and vibration characteristics of gears with early-stage crack (ESC), a coupling internal excitation model of gears with ESC and 3D errors is proposed based on a double-layer iterative method. In this model, the influence of meshing phase between tooth pair and along tooth width direction (TWD) on the time-varying mesh stiffness (TVMS) are simultaneously considered, which overcomes the limitations of traditional models that are only suitable for characterizing the coupled excitation of the two-dimensional tooth profile errors and TVMS. Based on verifying the proposed model, the effects of TSE type, parameters, and load on the TVMS, loaded static transmission error (LSTE), load distributions, and torsional deformation of the cracked gear teeth are investigated, and the vibration characteristics of ESC failures under the effects of 3D TSE are also explored. The research results show that the distribution parameters of 3D TSE have a significant effect on the internal excitation and vibration characteristics of gears with ESC, and thus measuring and evaluating the distribution parameters of TSE can help improve the accuracy of gear fault identification and diagnosis methods.

Three new methodologies for calculating the effective sample size when performing population adjustment

BackgroundThe concept of the population is of fundamental importance in epidemiology and statistics. In some instances, it is not possible to sample directly from the population of interest. Weighting is an established statistical approach for making inferences when the sample is not representative of this population.MethodsThe effective sample size (ESS) is a descriptive statistic that can be used to accompany this type of weighted statistical analysis. The ESS is an estimate of the sample size required by an unweighted sample that achieves the same level of precision as the weighted sample. The ESS therefore reflects the amount of information retained after weighting the data and is an intuitively appealing quantity to interpret, for example by those with little or no statistical training.ResultsThe conventional formula for calculating ESS is derived under strong assumptions, for example that outcome data are homoscedastic. This is not always true in practice, for example for survival data. We propose three new approaches to compute the ESS, that are valid for any type of data and weighted statistical analysis, and so can be applied more generally.ConclusionWe illustrate all methods using an example and conclude that our proposals should accompany, and potentially replace, the existing approach for computing the ESS.

JWST COMPASS: The 3–5 μm Transmission Spectrum of the Super-Earth L 98-59 c

We present a JWST Near-InfraRed Spectrograph (NIRSpec) transmission spectrum of the super-Earth exoplanet L 98-59 c. This small (R p = 1.385 ± 0.085R ⊕, M p = 2.22 ± 0.26 R ⊕), warm (T eq = 553 K) planet resides in a multiplanet system around a nearby, bright (J = 7.933) M3V star. We find that the transmission spectrum of L 98-59 c is featureless at the precision of our data. We achieve precisions of 22 ppm in NIRSpec G395H’s NRS1 detector and 36 ppm in the NRS2 detector at a resolution R ∼ 200 (30 pixel wide bins). At this level of precision, we are able rule out primordial H2–He atmospheres across a range of cloud pressure levels up to at least ∼0.1 mbar. By comparison to atmospheric forward models, we also rule out atmospheric metallicities below ∼300× solar at 3σ (or, equivalently, atmospheric mean molecular weights below ∼10 g mol−1). We also rule out pure methane atmospheres. The remaining scenarios that are compatible with our data include a planet with no atmosphere at all, or higher-mean-molecular-weight atmospheres, such as CO2- or H2O-rich atmospheres. This study adds to a growing body of evidence suggesting that planets ≲1.5 R ⊕ lack extended atmospheres.

Novel Dynamic Behaviors in Fractional Chaotic Systems: Numerical Simulations with Caputo Derivatives

Over the last several years, there has been a considerable improvement in the possible methods for solving fractional-order chaotic systems; however, achieving high accuracy remains a challenge. This work proposes a new precise numerical technique for fractional-order chaotic systems. Through simulations, we obtain new types of complex and previously undiscussed dynamic behaviors.These phenomena, not recognized in prior numerical results or theoretical estimations, underscore the unique dynamics present in fractional systems. We also study the effects of the fractional parameters β1, β2, and β3 on the system’s behavior, comparing them to integer-order derivatives. It has been demonstrated via the findings that the suggested technique is consistent with conventional numerical methods for integer-order systems while simultaneously providing an even higher level of precision. It is possible to demonstrate the efficacy and precision of this technique through simulations, which demonstrates that this method is useful for the investigation of complicated chaotic models.

High-throughput glycosaminoglycan extraction and UHPLC-MS/MS quantification in human biofluids.

Glycosaminoglycans (GAGs) are linear, unbranched heteropolysaccharides whose structural complexity determines their function. Accurate quantification of GAGs in biofluids at high throughput is relevant for numerous biomedical applications. However, because of the structural variability of GAGs in biofluids, existing protocols require complex pre-analytical procedures, have limited throughput and lack accuracy. Here, we describe the extraction and quantification of GAGs by using ultra-high-performance liquid chromatography coupled with triple-quadrupole mass spectrometry (UHPLC-MS/MS). Designed for 96-well plates, this method enables the processing of up to 82 study samples per plate, with the remaining 14 wells used for calibrators and controls. Key steps include the enzymatic depolymerization of GAGs, their derivatization with 2-aminoacridone and their quantification via UHPLC-MS/MS. Each plate can be analyzed in a single UHPLC-MS/MS run, offering the quantitative and scalable analysis of 17 disaccharides from chondroitin sulfate, heparan sulfate and hyaluronic acid, with a level of precision and reproducibility sufficient for their use as biomarkers. The procedure from sample thawing to initiating the UHPLC-MS/MS run can be completed in ~1.5 d plus 15 min of MS runtime per sample, and it is structured to fit within ordinary working shifts, thus making it a valuable tool for clinical laboratories seeking high-throughput analysis of GAGs. The protocol requires expertise in UHPLC-MS/MS.

Evaluation of various mass spectrometry mode based on gas chromatography for quantifying the polycyclic aromatic hydrocarbon metabolites in human urine

Hydroxyl polycyclic aromatic hydrocarbons (OH-PAHs) are frequently used as biomarkers to assess human exposure to polycyclic aromatic hydrocarbons (PAHs), which are typically present in minute concentrations in the human body. Although several studies have outlined methods for measuring OH-PAHs in human urine, these approaches may have limitations concerning the range of compounds detected, the sample volume required for pre-treatment, accuracy, and instrument maintenance costs. Advances in mass spectrometry technology have facilitated the use of various mass spectrometry modes based on gas chromatography. It is essential to evaluate different mass spectrometry modes to identify a more reliable and practical method for quantifying OH-PAHs in urine. Our comparative analysis of three gas chromatography-mass spectrometry modes revealed that advanced electron ionization (AEI) mode exhibited greater sensitivity for determination compared to electron ionization (EI) mode in tandem mass spectrometry. The method validation criteria were satisfied by GC-AEI-MS/MS, which demonstrated acceptable measurement errors (MEs) ranging from -23.0 % to 19.5 %, recoveries between 81.6 % and 112.1 %, and precision levels below 10.1 %. Furthermore, the reliability and accuracy of the GC-AEI-MS/MS method were corroborated through Intraclass Correlation Coefficient (ICC) evaluation (values > 0.75), participation in external proficiency testing programs (G-EQUAS), and analysis of certified reference materials, which exhibited a relative deviation of <6.1 %. In summary, GC-AEI-MS/MS is a robust and reliable method for analyzing urine samples and conducting large-scale human biological monitoring. It was employed to determine 16 OH-PAHs in human urine as part of the China National Human Biomonitoring Program (CNHBM).

Artificial intelligence neural network and fuzzy modelling of unsteady Sisko trihybrid nanofluids for cancer therapy with entropy insights

The main objective of the current endeavor is to monitor hypothetical processes utilizing a Sisko tri-hybrid fluid over a rotating disk with entropy generation suspended in Darcy-Forchheimer porous medium. Electro Magneto Hydro Dynamics (EMHD), non-linear thermal radiation and exponential and thermal- space dependent heat source/sink coefficients are considered with the intent of conceiving an Runge-Kutta-Fehlberg method with shooting procedures integrated with a combination of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Reptile Search Algorithm (RSA). Then, ANFIS-RSA, is used to predict the Nusselt number, skin friction co-efficient in radial and tangential velocities. Reliable self-similarity variables have reduced a non-linear partial differential set of equations into an ordinary differential equation. According to the empirical evidence, Sisko fluid parameter rises the radial velocity whereas for magnetic field and Darcy-Forchheimer the azimuthal and axial velocities visualizations decreasing trend, respectively. The entropy generation and Bejan number rises for electric and radiation effects. Also, ANFIS-RSA indicates that the model attained a high level of precision in terms of radial velocity (98.13%), tangential velocity (98.18%) and Nusselt number (98.91%). Thus, the longer rendering of the nanoparticles used here might, makes them potentially helpful for regulating the therapeutic impact in the management and treatment of cancer.

Investigation of the Effective Numerical Model for Seismic Response Analysis of Concrete-Faced Rockfill Dam on Deep Overburden

The construction of high rockfill dams on deep overburden in seismically active regions poses significant challenges. Currently, there are no standardized guidelines for defining the computational domain range in seismic analysis, necessitating the establishment of a universally applicable computational domain range that optimizes the balance between computational accuracy and efficiency. This has critical engineering implications for the seismic analysis of rockfill dams on deep overburden. This study employed the seismic wave input method to consider the dynamic interaction between the dam, overburden, and infinite domain. A systematic investigation was conducted on a concrete-faced rockfill dam (CFRD) constructed on deep overburden, considering the influences of overburden thickness, dam height, overburden properties, soil layer configuration, ground motion intensity, and the frequency content of the seismic waves. The acceleration response and seismic deformation of the dam were analyzed. Subsequently, the computational domain range corresponding to various levels of acceptable engineering precision was established. The results indicated that the lateral boundary length should extend a minimum distance equal to the sum of 3 times the overburden depth and 1.2 times the maximum dam height. Additionally, the depth below the overburden–bedrock interface should extend at least 1.2 times the maximum dam height. This study provides a crucial foundation for determining the optimal computational domain range in the seismic analysis of rockfill dams constructed on deep overburden.

Motion-induced phase shift for dynamic structured light measurement.

Structured light 3D shape measurement is extensively utilized in semiconductor inspection, smart manufacturing, and biomedical imaging due to its rapid measurement speed, high precision, and versatile applicability to different objects. However, the traditional implementations of this method often require that the object remains static while recording the phase-shifting structured light images, which limits the adaptability of dynamic measurement. Here, we propose a dynamic 3D shape measurement using structured light based on a motion-induced phase shift (MIPS). As the object moves, the surface features distort the fringe pattern, resulting in a phase-shifting effect. By employing the MIPS method, we can determine the phase even in the situations where the knowledge of phase-shifting conditions is not accurate. This enables the acquisition of the 3D topography of the object surface with a high level of precision. Experimental results demonstrate that the MIPS method can accurately measure the 3D shape of objects moving as fast as 100 mm/s, with a relative discrepancy of less than 0.23%.

Investigating the pH-Dependence of gelation process in chitosan-glutaraldehyde hydrogels with diffusing wave spectroscopy

The gel time of functional hydrogels is an essential property that dictates their utility in various applications. To accurately assess this parameter, the chitosan-glutaraldehyde hydrogels were characterized here using two complementary analytical tools, rotational rheometer, and diffusing wave spectrometer (DWS). Rotational rheometers are widely used to continuously monitor the macro-mechanical properties of hydrogels during the gelation process. By observing the modulus change curve, one can gain insights into the instantaneous changes occurring within the gel network. This method provides a direct measurement of the gel's mechanical strength; however, it involves applying external forces to the sample, which may introduce artifacts such as measurement lag and potential sample deformation or destruction. Results of this study indicated that the application of external force during rotary rheometer measurements disrupted the gelation process. In contrast, DWS detected alterations in the microstructure by measuring light diffusion, allowing to track microstructural changes post-crosslinking, and curing without the interference of external stress. Thus, DWS offers a non-invasive approach to studying gelation as it detected the Brownian motion of particles within the hydrogel by monitoring the diffraction of light, thereby providing undisturbed dynamic information about the gel's behavior. The DWS technique is particularly useful for tracking microstructural changes that occur during hydrogel crosslinking and curing, without altering the gel's native state. An additional advantage of DWS is its ability to investigate the pH effect on gelation. By measuring changes in gelation time as a function of pH, we could achieve precise control over the gelation process. This fine-tuning of gelation kinetics is crucial for applications that require rapid or delayed gelation, offering a level of precision that is difficult to match with traditional rheological methods.

Observation of ηc(2S)→K+K−η

By analyzing (27.12±0.14)×108 ψ(3686) events accumulated with the BESIII detector, the decay ηc(2S)→K+K−η is observed for the first time with a significance of 6.2σ after considering systematic uncertainties. The product of the branching fractions of ψ(3686)→γηc(2S) and ηc(2S)→K+K−η is measured to be B(ψ(3686)→γηc(2S))×B(ηc(2S)→K+K−η)=(2.39±0.32±0.34)×10−6, where the first uncertainty is statistical, and the second one is systematic. The branching fraction of ηc(2S)→K+K−η is determined to be B(ηc(2S)→K+K−η)=(3.42±0.46±0.48±2.44)×10−3, where the third uncertainty is due to the branching fraction of ψ(3686)→γηc(2S). Using a recent BESIII measurement of B(ηc(2S)→K+K−π0), we also determine the ratio between the branching fractions of ηc(2S)→K+K−η and ηc(2S)→K+K−π0 to be 1.49±0.22±0.25, which is consistent with the previous result of at a comparable precision level. Published by the American Physical Society 2024

A Multi-Objective Energy Optimization Model for Renewable Energy Systems with Hybrid Energy Storage

Building a Renewable Energy System (RES) is a viable way to address resource depletion and accomplish decarbonization. This study presents a hybrid power system that combines wind, solar, battery, and thermal energy storage. It also examines the co-optimization of scheduling and operation for multiple objectives. The hybrid system combines the affordability of thermal energy storage (TES) with the adaptability of batteries to effectively address the issue of intermittent RES. A new approach for integrated operation is offered, which relies on the electrical block's operation limit. The planning-operation co-optimization system considers minimizing the Net Present Cost (NPC) and reducing power supply likelihood todetermine the best operation limit and sizing decision factors. The co-optimization issue is addressed using novel multi-objective decision-making (MO-DM). This approach incorporates the Decision-Maker (DM) preferences data to direct the evolutionary process toward the desired area. In addition, a data-driven prediction model captures the risks and losses associated with wind generation. The case study's findings indicate the following: (1) The data-driven system has a higher level of precision in wind power projection when compared with commonly used physical simulations. (2) The suggested MO-DM exhibits superior integration, variety, and robustness achievement in the DMchosen area compared to others. (3) The combined battery-thermal energy storing structure achieves improved economy and dependability through the optimum organized operation approach, in contrast to using a single energy storage system under various testing constraints.

Multi-cylinder leveling control systems based on dual-valve parallel and adaptive eccentric torque suppression

In the composite material hydraulic press, the mismatched velocities between the movable beam and the multiple leveling cylinders produce disturbing superfluous forces, and the eccentric torque causes the movable beam to tilt when pressed. They severely damage the leveling displacement accuracy and limit the implementation of muti-cylinder system in higher precision field. A dual-loop leveling control strategy is proposed, comprising a dual-valve parallel pressure inner loop and an adaptive control displacement outer loop. Firstly, a dual-valve parallel scheme is proposed in the pressure inner loop, where a compensation valve is added in parallel with the original single-valve. A variable compensation valve spool algorithm is designed, considering both velocity and displacement to mitigate the effects of superfluous forces and achieve precise and smooth leveling. Secondly, a control strategy for the adaptive displacement outer loop is designed to estimate and compensate for eccentric torque. An innovative torque decoupling algorithm is formulated to overcome the challenge of indeterminate coupling relations between inner and outer loops caused by the adaptive incorporation of dual-loop control. Then, eccentric load compensation torque is decoupled to the multiple leveling cylinders and derives the desired pressure for the inner loop. The inner loop suppresses the disturbance of eccentric torque to enhance robust leveling precision. Finally, the effectiveness of the proposed strategy was validated through experimentation on the constructed hydraulic press leveling system test bench. The control strategy presented in this paper provides a reference for achieving smooth and precise control in multi-cylinder systems.

Revisiting the alcohol-aggression link: The impact of alcohol consumption patterns

Laboratory studies have repeatedly reported a link between alcohol and aggression, yet many rely on single-dose administration methods and overlook variations in alcohol consumption patterns. The present study investigates the effects of alcohol on aggressive behavior using a double-blind, placebo-controlled cumulative drinking administration approach that mirrors the natural drinking behaviors often observed in pubs within a laboratory setting. This study also pioneers the examination of how alcohol consumption patterns (light or heavy) moderate the relationship between precise Breath Alcohol Concentration (BrAC) levels and extreme aggressive behavior. Seventy-five individuals who drink alcohol lightly (N=38) and heavily (N=37) were randomly assigned to alcohol (N=33) or placebo (N=42) conditions. Participants drank four drinks successively. Taylor's aggression paradigm was completed twenty minutes after each drink. Multilevel modeling was applied to analyze the effects of precise BrAC and account for within-person variability. Alcohol showed a dose-dependent effect on aggression; as alcohol levels rose, so did aggression (p<.001). Alcohol consumption pattern moderated the effect of cumulative BrAC on aggression, such that the effect was more substantial for as compared with individuals who drink lightly (p=.03). The current study highlights the moderating role of alcohol consumption pattern in the association between precise BrAC levels and extreme aggression, offering insights into individual differences in susceptibility to alcohol-induced aggression, and supporting the I3 meta-theory (Finkel, 2014). The Findings underscore the importance of investigating the interplay between acute and chronic alcohol use on behavior, challenging conventional thresholds for hazardous drinking classification. Implications for future studies, legislators, and policymakers are discussed.