Acceleration Research Articles

Design of acceleration control schedule for adaptive cycle engine based on direct simulation model

To design the optimum acceleration control schedule for the Adaptive Cycle Engine (ACE) in the full flight envelope, this paper establishes a direct simulation model of the ACE transient state. In this model, geometric parameters are used to replace the component state parameters. The corresponding relationship between geometric parameters and component state parameters is determined by sensitivity analysis. The geometric variables are controlled when the geometric adjustment speed exceeds the limit, and at the same time the corresponding component state parameters are iterated. The gradient optimization algorism is used to optimize the ground acceleration process of ACE, and the control schedule in terms of operating point of compression components and corrected acceleration rate is used as the full-envelope acceleration control schedule based on the similarity principle. The acceleration control schedules of the triple-bypass mode and the double-bypass mode are designed in this paper. The acceleration processes under various flight conditions are simulated using the acceleration control schedules. Compared with the acceleration process with the linear geometric adjustment schedule, the acceleration performance of ACE is improved by the acceleration control schedule, with the impulse of the acceleration process of the triple-bypass mode being increased by 8.7%–12.3% and the impulse of the double-bypass mode acceleration process being increased by 11.8%–14.1%.

Internal or External Training Load Metrics: Which Is Best for Tracking Autonomic Nervous System Recovery and Function in Collegiate American Football?

Sport coaches increasingly rely on external load metrics for designing effective training programs. However, their accuracy in estimating internal load is inconsistent, and their ability to predict autonomic nervous system (ANS) deterioration is unknown. This study aimed to evaluate the relationships between internal and external training load metrics and ANS recovery and function in college football players. Football athletes were recruited from a D1 college in the southeastern US and prospectively followed for 27 weeks. Internal load was estimated via exercise cardiac load (ECL; average training heartrate (HR) × session duration) and measured with an armband monitor equipped with electrocardiographic capabilities (Warfighter MonitorTM (WFM), Tiger Tech Solutions, Miami, FL, USA). External load was estimated via the summation and rate of acceleration and decelerations as measured by a triaxial accelerometer using the WFM and an accelerometer-based (ACCEL) device (Catapult Player Load, Catapult Sports, Melbourne, Australia) worn on the mid-upper back. Baseline HR, HR variability (HRV) and HR recovery served as the indicators for ANS recovery and function, respectively. For HRV, two, time-domain metrics were measured: the standard deviation of the NN interval (SDNN) and root mean square of the standard deviation of the NN interval (rMSSD). Linear regression models evaluated the associations between ECL, ACCEL, and the indicators of ANS recovery and function acutely (24 h) and cumulatively (one- and two-week). Athletes (n = 71) were male and, on average, 21.3 ± 1.4 years of age. Acute ECL elicited stronger associations for 24 h baseline HR (R2 0.19 vs. 0.03), HR recovery (R2 0.38 vs. 0.07), SDNN (R2 0.19 vs. 0.02) and rMSSD (R2 0.19 vs. 0.02) compared to ACCEL. Similar results were found for one-week: 24 h baseline HR (R2 0.48 vs. 0.05), HR recovery (R2 0.55 vs. 0.05), SDNN (R2 0.47 vs. 0.05) and rMSSD (R2 0.47 vs. 0.05) and two-week cumulative exposures: 24 h baseline HR (R2 0.52 vs. 0.003), HR recovery (R2 0.57 vs. 0.05), SDNN (R2 0.52 vs. 0.003) and rMSSD (R2 0.52 vs. 0.002). Lastly, the ACCEL devices weakly correlated with ECL (rho = 0.47 and 0.43, p < 0.005). Our findings demonstrate that ACCEL poorly predicted ANS deterioration and underestimated internal training load. ACCEL devices may "miss" the finite window for preventing ANS deterioration by potentially misestimating training loads acutely and cumulatively.

Unveiling Definite and Indefinite Integrals Through Flexibly Using Different Formulas

One of the most powerful branches of mathematics is calculus. It is closely related with the study of changing rate and the quantity accumulation, which provides a framework for understanding and analyzing complex, continuous processes. Integral calculus and differential calculus are two main branches in calculus. Differential calculus focuses on the concept of derivatives, which represent the rate at which a quantity changes related to another variable. Derivatives are fundamental in understanding and describing processes that involve instantaneous rates of change, such as velocity, acceleration, and growth rates. By calculating derivatives, one can determine critical points, identify trends, and solve optimization problems. In contrast, integral calculus focuses on the notion of integrals, which signify the gathering of quantities within a given interval. Integrals allow people to find areas, volumes, and total amounts by summing infinitesimally small contributions. They are especially useful for solving problems involving accumulation, such as finding the area under a curve, determining total distance traveled, and calculating the net change in a quantity over a given interval.

Favourable peak diffraction shift moments as a function of Mg doping on ZnO matrix as a promising catalyst for methylene blue waste

The characterization of a novel Zinc oxide/Magnesium (4 %, 6 %, 8 %, and 10 %) composite via Sol-Gel method and the investigation of its modification properties has been discussed. Structural and optical properties was observed by X-Ray diffraction (XRD) and Fourier Transform Infra-red (FTIR), and then degradation performance has done evaluated by using UV–Visible (UV–Vis) spectrometer. The moment shifting and broadening of the diffraction peaks were clearly detected because of changes in strain and crystallite size. FTIR revealed meaningful the tuning of electron loss function and penetration depth that considerable increase in the efficiency of facilitate ease of light travel for stimulates the photocatalyst process. It found that ZnO/Mg (8 %) has the best degradation and acceleration rates (95.01 % in 15 min) for methylene blue solution were supported by the corresponding Mg atoms position, sharply peak penetration depth and high longitudinal optical (LO) vibration positions.

Impact of the Stator Coil Pitch on Acoustic Noise and Vibration of Squirrel Cage Induction Motors

In this article, the rate of acceleration, speed, and displacement of electric motor structure particles in response to radial electromagnetic forces is studied in detail. For this purpose, the vibrations and acoustic noise of three similar induction motors are investigated by changing the stator winding pitch through finite element modeling (FEM) by conducting electromagnetic, structural, and frequency analyses. Three similar induction motors with 24 slots in the stator are selected, two motors are wound with a shorted pitch by 2 and 1 slot, respectively, and the third motor's winding is considered full pitch winding. The simulation results are validated by experimental measurements of the amount of acoustic noise and vibrations of three motors. The results of the simulations and experimental measurements are in good agreement and indicate less noise and vibrations in the first induction motor with two short slots in the stator winding pitch.

Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging In Vivo.

Super-resolution ultrasound (SRUS) can image microvascular structure and flow at subwave-diffraction resolution based on localizing and tracking microbubbles (MBs). Currently, tracking MBs accurately under limited imaging frame rates and high MB concentrations remains a challenge, especially under the effect of cardiac pulsatility and in highly curved vessels. In this study, an acceleration-incorporated MB motion model is introduced into a Kalman tracking framework. The tracking performance was evaluated using simulated microvasculature with different MB motion parameters, concentrations, and acquisition frame rates, and in vivo human breast tumor US datasets. The simulation results show that the acceleration-based method outperformed the nonacceleration-based method at different levels of acceleration and acquisition frame rates and achieved significant improvement in true positive rate (TPR; up to 11.3%) and false negative rate (FNR; up to 13.2%). The proposed method can also reduce errors in vasculature reconstruction via the acceleration-based nonlinear interpolation, compared with linear interpolation (up to [Formula: see text]). The tracking results from temporally downsampled low frame rate in vivo datasets from human breast tumors show that the proposed method has better MB tracking performance than the baseline method, if using results from the initial high frame data as a reference. Finally, the acceleration estimated from tracking results also provides a spatial speed gradient map that may contain extra valuable diagnostic information.

Analysis of discretionary lane-changing behaviours of autonomous vehicles based on real-world data

This study aims to quantify the impact of discretionary lane-changing (DLC) on following vehicles (FVs) in the target lane using real-world dataset. The Waymo Open Dataset is used to identify the differences between autonomous vehicles (AVs) DLC and human-driven vehicles (HDVs) DLC maneuvers and compare their impacts on the driving volatility. Then, a block maxima (BM) model is applied to estimate crash risks. Finally, multivariate adaptive regression splines (MARS) is adopted to model gap acceptance behaviors of AV and HDV. Compared to HDV DLC, AV DLC leads to lower speed and yaw rate volatility and smaller acceleration rates of FVs. Further, the BM model reveals that the crash risk in AV DLC events is half of that in HDV DLC events. Additionally, MARS show that AV and HDV accept different lead gap. These findings highlight the benefits of mixing AVs in traffic and guide the improvement of AV controllers.

Growth kinetics of Lactococcus lactis and Lactobacillus casei in liquid culture medium containing as prebiotics inulin or fructose.

Predictive microbiology is a tool that allows us to evaluate the behavior of the concentration of biomass and estimated cells under extrinsic conditions, providing scientific and industrial benefits. In the present study, the growth of L. lactis and L. casei combined with inulin and fructose was modeled using the Gompertz sigmoidal growth functions and plotted using data obtained from batch culture in relation to biomass and cell concentration expressed as estimates in ln N (OD600nm and cells mL-1 ) as a function of time. The results of the kinetic modeling indicated that (T1) A1B1 = L. lactis + fructose and (T4) A2B2 = L. casei + inulin presented the best function coefficients and best fits in most cases compared to the rest. The specific growth rate of the maximum acceleration was from 0.364 to 0.473 h-1 and 0.100 to 0.129 h-1 , the concentration of bacterial cells (A) was from 0.556 to 0.713 and 0.425 to 0.548 respectively and the time where (μ) occurred with a greater magnitude (L) fluctuated between 0.854 and 0.802 and when this time in (L) is very fast, it presents values of ≤0.072 to ≤0.092. Its coefficient of determination and/or multiple regression (R2 ) obtained in the two adjustments was 0.97. It was possible to predict the influence of the carbon source on the behavior of maximum growth rates, higher consumption due to nutrient affinity and shorter growth time. © 2023 Society of Chemical Industry.

Photocatalytic Scheme with External Magnetic Field on Coffee Waste in Hydrogen Production

Photocatalytic is considered a deadlock-breaking technology for renewable energy and lowering environmental pollutants. Photocatalytic efficiency efforts are improved by activating the photocatalyst to introduce an external magnetic field. This review highlights recent breakthroughs by comparing original coffee waste, activation of coffee waste with the addition of catalysts, and manipulation of spin electrons by applying external magnets. Characterization was done with FTIR to look at chemical bonds, UV-Vis with Tauc's Relation approach to measure bandgap, and material morphology using SEM EDX. The increase in photocatalyst activation aligns with the decrease in bandgap value. The rate of decline in bandgap is in line with the rate of acceleration of hydrogen production. Adding an external magnetic increases hydrogen production up to 1.5 times greater than the original photocatalytic.

Advancing classroom fatigue recognition: A multimodal fusion approach using self-attention mechanism

This paper presents a novel multimodal tensor fusion network based on the self-attention mechanism (MTFN-SAM) for classroom fatigue recognition. The LSTM network is first employed to learn the fatigue information of different modalities over time. Then, a self-attention mechanism is utilized to capture the connections within the modal features. Finally, a multimodal tensor fusion layer is employed to fuse features from multiple modalities, including electroencephalogram (EEG), heart rate (HR), galvanic skin response (GSR), and acceleration (ACC). The classification accuracy of the experimental results was compared with TFN, LWF, LSTM, and other fusion algorithms, confirming that the proposed MTFN-SAM network had better performance in multimodal feature fusion. Additionally, we found that as the number of modalities increased, the information contained in the multimodal fusion became more abundant, as confirmed by the experimental results (94.11% accuracy after EEG+HR+GSR+ACC fusion). Thus, the method could efficiently identify students’ classroom fatigue.

A multi-time-scale wall model for large-eddy simulations and applications to non-equilibrium channel flows

The recent Lagrangian relaxation towards equilibrium (LaRTE) approach (Fowleret al.,J. Fluid Mech., vol. 934, 2022, A44) is a wall model for large-eddy simulations (LES) that isolates quasi-equilibrium wall-stress dynamics from non-equilibrium responses to time-varying LES inputs. Non-equilibrium physics can then be modelled separately, such as the laminar Stokes layers that form in the viscous region and generate rapid wall-stress responses to fast changes in the pressure gradient. To capture additional wall-stress contributions due to near-wall turbulent eddies, a model term motivated by the attached eddy hypothesis is proposed. The total modelled wall stress thus includes contributions from various processes operating at different time scales (i.e. the LaRTE quasi-equilibrium plus laminar and turbulent non-equilibrium wall stresses) and is called the multi-time-scale (MTS) wall model. It is applied in LES of turbulent channel flow subject to a wide range of unsteady conditions from quasi-equilibrium to non-equilibrium. Flows considered include pulsating and linearly accelerating channel flow for several forcing frequencies and acceleration rates, respectively. We also revisit the sudden spanwise pressure gradient flow (considered in Fowleret al.,J. Fluid Mech., vol. 934, 2022, A44) to review how the newly introduced model features affect this flow. Results obtained with the MTS wall model show good agreement with direct numerical simulation data over a vast range of conditions in these various non-equilibrium channel flows. To further understand the MTS model, we also describe and test the instantaneous-equilibrium limit of the MTS wall model. In this limit, good wall-stress predictions are obtained with reduced model complexity but providing less complete information about the wall-stress physics.

Real-world performance of a fully automatic antitachycardia pacing algorithm

Abstract Background Antitachycardia pacing (ATP) found in most implantable cardioverter defibrillators (ICDs) is an important therapy for painless termination of ventricular tachycardias (VT). In ICDs programmed to guideline-recommended detection and therapy settings typical ATP success rates are 50-70%.[1-3] The first closed-loop, automated ATP (AATP) algorithm, introduced in 2021, applies an S1 train derived from the detected VT, followed by an S2 derived initially from a refractory period estimator and then adjusted using analysis of the post-pacing interval if ATP fails. The termination efficacy of AATP in the real-world population is unknown. Purpose To assess the effectiveness AATP in a large real-world population. Methods A database of de-identified ICD transmissions from subjects in the United States, Australia, Canada, and New Zealand was queried for AATP treated episodes. ICDs were randomly sampled, biased toward ICDs with multiple treated VT episodes. For rhythms classified as monomorphic ventricular tachycardia, each AATP pulse train was reviewed for: rhythm at delivery, effect of ATP, and any non-capturing pulses. Acceleration was defined as a post-ATP decrease in MVT cycle length (VTCL) of at least 10% and at least 30ms. Acceleration to polymorphic VT or ventricular fibrillation (PVT/VF) was assessed. Rates of termination and acceleration were calculated for all MVT and dichotomized using a 320ms VTCL cutoff. The method of generalized estimating equations (GEE) was used to correct event rates for multiple episodes from a single patient. Results There were 2281 episodes of MVT in 340 patients; 63% of patients had 2 or more episodes. There were 724 episodes with initial VTCL &amp;lt;320ms and 1557 episodes with VTCL ≥320ms. The rate of termination of MVT by AATP was 78% for all VTCL [95% CI: 74%-81%]. In 300 patients (88%) AATP terminated at least one MVT. For MVT with VTCL ≥320ms the termination rate was 80% [95% CI: 75%-83%] and for VTCL &amp;lt;320ms the termination rate was 75% [95% CI: 69%-80%]. The rate of acceleration was 3.6% [95% CI: 2.7%-4.9%], with a 0.8% rate of acceleration to PVT/VF [95% CI: 0.4%-1.8%]. Acceleration led to shock therapy in 63% of accelerated episodes. All MVT accelerated to PVT/VF either spontaneously terminated (4/14) or were successfully converted by a single shock therapy (10/14). All accelerations that remained MVT were successfully treated by the device with either subsequent ATP (35%) or shock therapy (61%), or spontaneously terminated (4%). Conclusion In this large, real-world cohort analysis, automated ATP’s termination rate exceeded the typical success rate of burst ATP, particularly for MVT &amp;lt;320ms. Acceleration to PVT/VF was low and effective first-shock defibrillation was consistent. Subsequent ATP for accelerated MVT had incremental benefit, leading to successful termination one-third of the time. Additional data are currently being prospectively collected in a separate post-approval study.

Efficient nonlinear homogenization of bones using a cluster-based model order reduction technique.

We present a reduced order model for efficient nonlinear homogenization of bones, accounting for strength difference effects and containing some well-known plasticity models (like von Mises or Drucker-Prager) as special cases. The reduced order homogenization is done by using a cluster-based model order reduction technique, called cluster-based nonuniform transformation field analysis. For an offline phase, a space-time decomposition is performed on the mesoscopic plastic strain fields, while a clustering analysis is employed for a spatial decomposition of the mesoscale RVE model. A volumetric-deviatoric split is additionally introduced to capture the enriched characteristics of the mesoscopic plastic strain fields. For an online analysis, the reduced order model is formulated in a unified minimization problem, which is compatible with a large variety of material models. Both cortical and trabecular bones are considered for numerical experiments. Compared to conventional FE-based RVE computations, the developed reduced order model renders a considerable acceleration rate beyond , while maintaining a sufficient accuracy level.

Aortic stenosis rapid progression is associated with premature cardiac damage independently of baseline severity

Abstract Background Aortic stenosis (AS) is one of the major valvular heart diseases in developed countries. Degenerative fibrocalcific AS is a progressive disease of the valve and, ultimately, the myocardium. AS is a potentially fatal disease characterized not only by a narrowing of the aortic orifice but also by gradual cardiac damage (CD) that may extend beyond the left ventricle (LV). However, the presence and extent of extra-aortic cardiac damage did not reflect the baseline hemodynamic severity of AS in retrospective studies. Purpose This study aims to determine the rate of AS progression and its impact on cardiac damage and survival. Methods We retrospectively identified 914 patients (age 76 ± 8 years, 52% female, median follow-up time 6.8 years) with AS who had undergone &amp;gt; 1 echocardiogram. Bayesian hierarchical nonlinear models were used to predict aortic peak velocity as a function of time and estimate individual AS acceleration rates. Then, patients were clustered into rapid (RP) and slow progressors (SP) using machine learning algorithms. Results APV was best modeled by a logistic function of time. 483 patients were clustered as RP (53 %) and 431 as SP (47 %), with acceleration rate coefficients estimated at 0.14 ± 0.02 years-1 and 0.09 ± 0.02 years-1, respectively (p &amp;lt; 0.01). No association between progression rate and clinical variables was found. Compared with SP, RP had significantly higher 5-year incidences of LV damage, combined left atrium and mitral valve damage and combined tricuspid valve damage and pulmonary hypertension (all p ≤ 0.01). No statistically significant differences were seen for right ventricle damage between RP and SP, as the number of events was low. After multivariate adjustment for age, gender, comorbidities and baseline AS severity, RP remained an independent predictor for all extra-aortic cardiac damages except for the RV dysfunction. Importantly, baseline AS severity was not predictive of AS-related CD. RP was associated with higher mortality (HR 1.28, p = 0.02), persisting after adjustment for demographics, comorbidities, AS severity, and time-dependent aortic valve replacement (AVR) (HR = 1.36, p &amp;lt; 0.01). Conclusions RP may impair cardiac adaption to rapidly changing loading conditions, which induces premature cardiac damage that may not be reversible in the late stages of AS, when guideline-triggered interventions are currently delivered.

Improving risk stratification of moderate aortic stenosis

Abstract Background The clinical course of patients with moderate aortic stenosis (AS) remains ill-defined. While a watchful waiting approach is advocated by international guidelines, several studies suggest that the long-term prognosis of these patients is worse than previously considered and may even be similar to patients with severe AS. Whether these findings reflect a subgroup of moderate AS with a high comorbid burden or rapid progression to severe AS is not known. Purpose This study sought to 1) analyze the clinical course of AS throughout all stages of the disease, 2) unveil different trajectories of progression through clustering analysis, 3) explore their impact in all-cause death. Methods Patients with mild to severe AS in seriated echocardiograms (TTE) were retrospectively identified after excluding for abnormal flow conditions. Aortic peak velocity (APV) was used to monitor disease progression. A sigmoid function was used to model APV throughout time. Individual acceleration rates coefficients were calculated using random-effects models and patients were then clustered into rapid (RP) and slow progressors (SP). Results This study included 914 patients (age 76 ± 8 years, 52% female, median follow-up time 6.8 years). 483 patients were clustered as RP (53%) and 431 as SP (47%) based on their individual acceleration rate coefficients (core parameter of sigmoid model). Since the core parameter is constant throughout time and evaluations, only two consecutive TTEs per patient are needed to calculate the acceleration rate coefficient. The time interval required between these seriated TTEs varies between 9 and 14 months. No clinical variables were consistently associated with each cluster. When comparing mortality hazard rates for SP with RP across the range of APV, we found that rapid progression was persistently associated with excess mortality (HR 1.35, CI [1.07-1.69]; p=0.01; after adjusting for demographics, comorbidities, baseline AS severity, and time-dependent aortic valve replacement [AVR]). Using the upper limit of the mortality rate of a SP with an APV of 4 m/s as a reference, we found that a RP significantly exceeded this threshold with an APV velocity of only 3.2 m/s. We built a predictive model which incorporated the AS progression on top of guideline-derived AVR triggers to assign patients with a predicted probability of death ≥ 50% at 5 years to AVR. Using this model, we estimated that an extra death can be prevented for every 100 patients treated without exposing more patients to unnecessary AVR. Conclusion We identified two clusters of AS progressors, based on APV from two consecutive TTEs. RP displayed higher mortality rates across all the range of APV which can explain why same patients with moderate AS have a dismal prognosis. Since RP patients have excess adverse outcomes in early stages of AS (moderate AS), a lower APV threshold may be required to better reflect the prognosis of AS and trigger treatment.

Multi-phase simulation for understanding morphodynamics of gravel beaches

This paper presents the use of an Eulerian–Eulerian multi-phase model to examine sediment transport over a gravel beach under regular waves. A previously published full-scale experiment is reproduced numerically; the simulated wave deformation, flow velocity, pore pressure, and morphological evolution are consistent with measurements. The multi-phase model can produce berm formation for smaller waves and beach erosion for larger waves as observation in previous field studies. The sensitivity analysis suggests that the nonlinear component of drag has significant effect on the berm formation through infiltration and exfiltration. The two simulated forces applied on the sediment (drag and buoyancy) are examined. The drag is the main force directly for sediment transport; therefore the sediment transport rate has high correlation with depth-averaged flow velocity. The buoyancy that is caused by plunging flow impacting beach face can push the sediment landward in the bore front, where the flow acceleration has its peak. The flow acceleration is considered in some previous sediment transport models empirically for simulating morphological evolution of gravel beach under waves. The buoyancy links the flow acceleration and sediment transport rate in the bore front.

Frequency Learning via Multi-scale Fourier Transformer for MRI Reconstruction.

Since Magnetic Resonance Imaging(MRI) requires a long acquisition time, various methods were proposed to reduce the time, but they ignored the frequency information and non-local similarity, so that they failed to reconstruct images with a clear structure. In this paper, we propose Frequency Learning via Multi-scale Fourier Transformer for MRI Reconstruction(FMTNet), which focuses on repairing the low-frequency and high-frequency information. Specifically, FMTNet is composed of a high-frequency learning branch(HFLB) and a low-frequency learning branch(LFLB). Meanwhile, we propose a Multi-scale Fourier Transformer(MFT) as the basic module to learn the non-local information. Unlike normal Transformers, MFT adopts Fourier convolution to replace self-attention to efficiently learn global information. Moreover, we further introduce a multi-scale learning and cross-scale linear fusion strategy in MFT to interact information between features of different scales and strengthen the representation of features. Compared with normal Transformers, the proposed MFT occupies fewer computing resources. Based on MFT, we design a Residual Multi-scale Fourier Transformer module as the main component of HFLB and LFLB. We conduct several experiments under different acceleration rates and different sampling patterns on different datasets, and the experiment results show that our method is superior to the previous state-of-the-art method. The code and dataset will be available at: https://github.com/Joyies/FMTNet.

Active Preference Learning for Vehicle Suspensions Calibration

In control system design, the parameters of the feedback action are often computed as the solution of an optimization problem, in which the objective function is defined a priori by the end user. However, in many vehicle control applications, the driver preferences are subjective and hard to mathematically model, thus hours of driving are often needed for end-of-line (EoL) calibration. In this brief, we propose a purely model-free design strategy for vehicle suspensions based on active preference learning (APL). In this method, the controller is tuned only via few dedicated experiments, corresponding to different calibrations and the related user preferences. Since the lack of formal performance metrics might raise concerns about the optimality of the approach, a validation procedure is also proposed, in which an interpretable performance index weighting vertical acceleration and pitch rate is learned from data. This latent index is used to compare the proposed approach with state-of-the-art data-driven optimization.

The Impact of Fluoroethylene Carbonate Additive on Charged Sodium Ion Electrodes/Electrolyte Reactivity Studied Using Accelerating Rate Calorimetry

The effects of fluoroethylene carbonate (FEC) electrolyte additive on charged sodium ion electrode/electrolyte reactivity at elevated temperatures were investigated using accelerating rate calorimetry (ARC). The beneficial effect of FEC on cell lifetime was demonstrated using Na0.97Ca0.03[Mn0.39Fe0.31Ni0.22Zn0.08]O2 (NCMFNZO)/hard carbon (HC) pouch cells first prior to ARC measurements. Electrodes from these pouch cells were utilized as sample materials and 1.0 M NaPF6 in propylene carbonate (PC):ethyl methyl carbonate (EMC) (1:1 by vol.) was chosen as control electrolyte. Adding 2 wt% and 5 wt% FEC to the electrolyte does not significantly affect the reactivity of de-sodiated NCMFNZO compared to the control electrolyte. However, the addition of FEC obviously changed the reactivity between sodiated HC and electrolytes, especially by showing a suppression on the exothermal behavior between 160 °C and 230 °C. These results give a head to head comparison of the reactivity of FEC additive containing electrolytes with charged sodium ion electrode materials at elevated temperatures and show that the use of FEC at additive levels should not compromise the cell safety when extending cell lifetime.

A fast parallelized DBSCAN algorithm based on OpenMp for detection of criminals on streaming services.

Streaming services are highly popular today. Millions of people watch live streams or videos and listen to music. One of the most popular streaming platforms is Twitch, and data from this type of service can be a good example for applying the parallel DBSCAN algorithm proposed in this paper. Unlike the classical approach to neighbor search, the proposed one avoids redundancy, i.e., the repetition of the same calculations. At the same time, this algorithm is based on the classical DBSCAN method with a full search for all neighbors, parallelization by subtasks, and OpenMP parallel computing technology. In this work, without reducing the accuracy, we managed to speed up the solution based on the DBSCAN algorithm when analyzing medium-sized data. As a result, the acceleration rate tends to the number of cores of a multicore computer system and the efficiency to one. Before conducting numerical experiments, theoretical estimates of speed-up and efficiency were obtained, and they aligned with the results obtained, confirming their validity. The quality of the performed clustering was verified using the silhouette value. All experiments were conducted using different percentages of medium-sized datasets. The prospects of applying the proposed algorithm can be obtained in various fields such as advertising, marketing, cybersecurity, and sociology. It is worth mentioning that datasets of this kind are often used for detecting fraud on the Internet, making an algorithm capable of considering all neighbors a useful tool for such research.