Current Step Research Articles

Quantum dynamics of a 4π kink in Josephson junction parallel arrays with large kinetic inductance

We present a theoretical study of the quantum dynamics of two magnetic fluxons trapped in Josephson junction parallel arrays (JJPAs) with large kinetic inductances. The Josephson phase distribution of two trapped magnetic fluxons satisfies a topological constraint, i.e., the total variation of Josephson phases along a JJPA is $4\ensuremath{\pi}$. In such JJPAs the characteristic length of the Josephson phase distribution (``the size'' of magnetic fluxon) is drastically reduced to be less than a single cell size. Two extreme dynamic patterns will be distinguished: two weakly interacting and two merged magnetic fluxons, i.e., a $4\ensuremath{\pi}$ kink. Taking into account the repulsive interaction between two magnetic fluxons located in the same or adjacent cells, we obtain the energy band spectrum ${E}_{4\ensuremath{\pi}}(p)$ for a quantum $4\ensuremath{\pi}$ kink. The coherent quantum dynamics of $4\ensuremath{\pi}$ kinks demonstrates the quantum beats with the frequency and amplitude strongly deviating from those observed for two independent magnetic fluxons. In the presence of applied dc and ac bias currents of frequency $f$ a weakly incoherent quantum dynamics of a $4\ensuremath{\pi}$ kink results in the Bloch oscillations and the seminal current steps with values ${I}_{4\ensuremath{\pi}}^{(n)}=enf$ which are two times less than those for two independent magnetic fluxons.

Core node knowledge based multi-objective particle swarm optimization for dynamic community detection

Community detection aims to classify nodes with similar attributes, and it becomes more difficult when the network structure changes over time. In order to tap into the evolution of dynamic networks, nodes with high criticality in dynamic networks clearly need to be attended. In this paper, a novel dynamic community detection algorithm based on particle swarm optimization is proposed. The core nodes in the network are obtained by calculating the resistance distance of each node. The nodes associated with these core nodes that make up the constant community. The constant community should be retained is the knowledge gained from the evolution of the core nodes in two consecutive time steps. To ensure the quality of community division at the current time step and the smoothness of two consecutive time steps, a new particle updating parameter is proposed, which is based on the optimal individual at the previous time step and the population at the current time step. Compared with the other well-known related algorithms, experimental result on many real-world and synthetic networks have illustrated the higher accuracy and stability of the proposed.

High dimensional multistep deblending using supervised training and transfer learning

Blended acquisition significantly improves acquisition efficiency, and deblending algorithms, particularly intelligent deblending methods, continue to be developed to provide separated results for subsequent seismic inversion and imaging. Iterative deblending algorithms improve deblending performance and determining how to evaluate the blending noise level becomes critical. Instead of using a multilevel blending noise strategy to evaluate the blending noise level qualitatively, a supervised multistep deblending algorithm is developed that can evaluate the blending noise level quantitatively in multiple steps. The developed multistep method combines the iterative estimation-subtraction strategy based on sparse inversion and the deep learning strategy. Each deblending step handles a different level of blending noise, ranging from a strong level to a weak level as the deblending steps increase. The first step is to train a U-net to attenuate strong blending noise, and then we can obtain a rough signal estimation for predicting the blending noise to be subtracted. The obtained data, via the blending noise estimation and subtraction following the previous deblending step, are used as the input for the current deblending step, which attenuates weak blending noise and extracts signal leakage in a step-by-step manner. The optimized parameters of the previous deblending step can initialize the current step for efficient fine-tuning based on transfer learning. After sequential blending noise estimation and subtraction, the supervised multistep deblending algorithm with varying input can improve deblending accuracy. A thorough examination of 2D and 3D synthetic blended data demonstrates the validity of our multistep deblending method, particularly when compared with the recently proposed multilevel blending noise strategy. The 3D field blended data processing validates our method in terms of removing blending noise while preserving the signal.

Evaluation of the orientation impact on thermal behavior of cylindrical Li-ion batteries in different cycling conditions using fiber Bragg grating sensors

In this work, optical fiber Bragg grating (FBG) sensors were used to evaluate the thermal performance of an 18650 Li-ion battery (LiB) operating under normal and abusive conditions while positioned in horizontal and vertical orientations. In total, three FBG sensors were used to track in real-time the temperature variation of the cathode, middle, and anode of the LiB. Tests for each orientation were performed, where each of them consisted of two cycles: the first one with normal charge/discharge conditions, operating between 2.75 V and 4.20 V, and another in abusive conditions, operating between 2.00 V and 4.95 V. The battery was submitted to constant current charge and discharge steps, with rest intervals between each operation. The results suggest that, in general, the temperature variation while operation in vertical orientation is lower when compared to the horizontal one, mainly in the anode, while the LiB is submitted to the abusive charge procedure, where the temperature variation difference achieved 3.4 ± 0.2 °C. In addition, the FBG sensors were able to track in real-time the temperature variation of three different locations of the battery simultaneously. The temperature variation registered for each sensor is shown and discussed.

The spatiotemporal coupling in delay-coordinates dynamic mode decomposition.

Dynamic mode decomposition (DMD) is a leading tool for equation-free analysis of high-dimensional dynamical systems from observations. In this work, we focus on a combination of DMD and delay-coordinates embedding, which is termed delay-coordinates DMD and is based on augmenting observations from current and past time steps, accommodating the analysis of a broad family of observations. An important utility of DMD is the compact and reduced-order spectral representation of observations in terms of the DMD eigenvalues and modes, where the temporal information is separated from the spatial information. From a spatiotemporal viewpoint, we show that when DMD is applied to delay-coordinates embedding, temporal information is intertwined with spatial information, inducing a particular spectral structure on the DMD components. We formulate and analyze this structure, which we term the spatiotemporal coupling in delay-coordinates DMD. Based on this spatiotemporal coupling, we propose a new method for DMD components selection. When using delay-coordinates DMD that comprises redundant modes, this selection is an essential step for obtaining a compact and reduced-order representation of the observations. We demonstrate our method on noisy simulated signals and various dynamical systems and show superior component selection compared to a commonly used method that relies on the amplitudes of the modes.

A data augmentation based Kriging-assisted reference vector guided evolutionary algorithm for expensive dynamic multi-objective optimization

Expensive dynamic multi-objective optimization problems (EXDMOPs) involve multiple objective functions changing over time steps. In this kind of problems, only a small number of function evaluations can be allowed in each time step. The challenge of EXDMOP is how to quickly and accurately track the changing optimal solutions with only a small number of function evaluations. Although EXDMOP can be solved as a series of independent expensive multi-objective optimization problems or as an ordinary dynamic multi-objective optimization problems, these two methods either ignore the connection between different time steps or degrade the performance significantly due to the requirement for a large number of function evaluations. In this paper, we incorporate two proposed strategies named training data augmentation and sampling pool expansion to the Kriging-assisted reference vector guided evolutionary algorithm to construct a novel algorithm for solving EXDMOPs. The training data augmentation strategy tries to look for the previous similar time step, and adds the solutions evaluated by the objective functions at that time step to current time step. The accuracies of the Kriging models are improved in this way. The sampling pool expansion strategy sends the predicted optimal solutions to the competition of the function evaluations assignment at each time step, where the predicted optimal solutions are generated by the improved population prediction strategy. We set the ablation experiment for the proposed algorithm and compare it with four dynamic multi-objective evolutionary algorithms on various of problems. The experiment results validate the effectiveness of the proposed two strategies. It also indicates that the proposed algorithm is promising for solving EXDMOPs.

A 12V/24V-to-1V PWM-Controlled DSD Converter With Delay-Insensitive and Dual-Phase Charging Techniques for Fast Transient Responses

A 12V/24V-to-1V pulsewidth modulation (PWM) controlled double step-down (DSD) power converter with delay-insensitive and dual-phase charging techniques is presented. The proposed PWM controller has two feedback loops to regulate the output voltage and the voltage across the flying capacitor, respectively. With the proposed delay-insensitive technique, near-optimal transient responses are achieved by using the PWM control regardless of the time of occurrence of the transient. Moreover, the responses are further improved by two times by using the proposed dual-phase charging technique. The converter was fabricated in a 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> BCD process with all power transistors integrated on chip. For 12V-to-1V conversion and 24V-to-1V conversion, the measured peak efficiencies at 1-MHz switching frequency are 88.3% and 83.5%, respectively. With a load current step of 3 A and a rise time of 20 ns, the measured voltage droop and 1% settling time are 56 mV and 0.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> , respectively.

Incremental Zero-Shot Learning.

The goal of zero-shot learning (ZSL) is to recognize objects from unseen classes correctly without corresponding training samples. The existing ZSL methods are trained on a set of predefined classes and do not have the ability to learn from a stream of training data. However, in many real-world applications, training data are collected incrementally; this is one of the main reasons why ZSL methods cannot be applied to certain real-world situations. Accordingly, in order to handle practical learning tasks of this kind, we introduce a novel ZSL setting, referred to as incremental ZSL (IZSL), the goal of which is to accumulate historical knowledge and alleviate Catastrophic Forgetting to facilitate better recognition when incrementally trained on new classes. We further propose a novel method to realize IZSL, which employs a generative replay strategy to produce virtual samples of previously seen classes. The historical knowledge is then transferred from the former learning step to the current step through joint training on both real new and virtual old data. Subsequently, a knowledge distillation strategy is leveraged to distill the knowledge from the former model to the current model, which regularizes the training process of the current model. In addition, our method can be flexibly equipped with the most generative-ZSL methods to tackle IZSL. Extensive experiments on three challenging benchmarks indicate that the proposed method can effectively tackle the IZSL problem effectively, while the existing ZSL methods fail.

A 95.1% Efficiency Hybrid Hysteretic Reconfigurable 3-Level Buck Converter With Improved Load Transient Response

This article presents a hybrid hysteretic reconfigurable 3-level buck converter for improving load transient response. A reconfigurable 3-level mode operation is proposed to improve the load transient response without the voltage rating issue. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> feedback network of the hybrid hysteretic control can be adjusted for the optimized transient response. Furthermore, a hysteresis-based quasi-self-balancing scheme, keeping the voltage across the flying capacitor at half the input voltage, is proposed to guarantee stable operation of the proposed converter. Implemented in a 55 nm CMOS, the proposed converter achieves maximum efficiency of 95.1% at an input voltage of 4.2 V, an output voltage of 3.3 V, and a load current of 200 mA. For a load current step of 750 mA, the under/overshoot voltage and settling times are 62/60 mV and 2.48/2.32 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> s, respectively. Compared to the conventional 3-level mode operation, the proposed technique improves the under/overshoot voltage and settling time by 2.21/2.22 and 1.78/1.69 times, respectively.

Estimating Average Velocities of Particle Arrival Using the Time Duration of the Current Signal in Stochastic Blocking Electrochemistry.

In stochastic blocking electrochemistry, microparticles generate individual current steps when they adsorb on a microelectrode and decrease the current and flux of a redox mediator reacting at the surface. The amplitude of the current step informs on particle size and landing locus, while step frequency correlates with particle transport. Here, we report a new method to estimate the average arrival velocities of single rod-shaped bacteria (bacilli). The method relies on simulating the nearby threshold distance from the surface where the bacillus no longer perturbs mediator flux and the current step approaches zero. We estimated the average velocities of bacillus arrival by dividing the threshold distance over the current step duration, a parameter that here we detect for the first time and increases with bacillus length. By comparing diffusional fluctuations to bacillus average velocity, we estimated diffusion and migration contributions as a function of bacterium size. Average arrival velocities increase with bacillus length at the same time as migration intensifies and diffusion weakens. Our analysis is universal and more effective in determining transport mode contributions than the present approach of comparing theoretical and experimental step frequencies. Uncertainty in landing locus is inconsequential because the step duration used to calculate the average arrival speed already contains such information and knowing bacillus electrophoretic mobility or ζ-potential is not needed. Additionally, by simulating and assigning edge landings to the most repeated values of current steps in a recording, we obtain bacilli lengths and widths similar to scanning electron microscopy, from which we infer landing orientation.

Experimental analysis of Cascade CSTRs with step and pulse inputs

In industrial reactors and equipment, non-ideality is quite a common phenomenon rather than an exception. These deviations from ideality impact the process's overall efficiency and the effectiveness of the equipment. To recognize the associated non-ideality, one needs to have enough understanding of the formulation of the equations and in-depth knowledge of the residence time distribution (RTD) data of real reactors. In the current work, step input and pulse input were used to create RTD data for Cascade continuous stirred tank reactors (CSTRs). For the aforementioned configuration, experiments were run at various flow rates to validate the developed characteristic equations. To produce RTD data, distilled water was utilized as the flowing fluid, and NaOH was the tracer substance. The ideal behavior of tracer concentration exits age distribution, and cumulative fraction for each setup and each input was plotted and experimental results were compared with perfect behavior. Deviation of concentration exit age distribution and cumulative fractional distribution from ideal behavior is more in pulse input as compared to a step input. For ideal cases, the exit age distribution curve and cumulative fraction curves are independent of the type of input. But a significant difference was observed for the two cases, which may be due to non-measurable fluctuations in volumetric flow rate, non-achievement of instant injection of tracer in case of pulse input, and slight variations in the sampling period. Further, with increasing flow rate, concentration, exit age, and cumulative fractional curves shifted upward, and this behavior matches with the actual case.

Error-output recurrent multi-layer Kernel Reservoir Network for electricity load time series forecasting

Electricity is one of the most consumed commodities in the modern world. Electricity load prediction models are used to plan distribution operations to balance the equilibrium of demand and supply. This necessity has increased the number of recent research works. They employed several learning algorithms, such as support vector regression, to predict demands. However, these algorithms have high computational cost and too many user-defined parameters that directly impact their performance. Recently, randomization-based learning algorithms have been widely tested because they performed well at a lower cost. However, still, there was a main drawback: uncertainty in approximation and learning. This work employed a kernel trick to solve the uncertainty problem. A kernel with reservoir-state layers was used to solve the problem. The kernel reservoir-state layers from the echo state network not only transformed features into high-dimensional space, but also enhanced the forecasting ability by learning temporal information. Additionally, the proposed model also had a multi-step prediction ability that used previous forecasting errors to update the output weights in the current step to prevent an accumulated error problem. We compared our proposed model with single-layer and multi-layer variants of Extreme Learning Machine, Echo State Network, and Random Vector Functional Link on ten electrical load data sets. The proposed model showed the best performance on 9/10 data sets in terms of Mean Square Error or Symmetric Mean Absolute Percentage Error. These findings implied that the proposed algorithm was superior in forecasting long-term electricity load.

Inconsistency evaluation of vehicle-oriented fuel cell stacks based on electrochemical impedance under dynamic operating conditions

The dynamic inconsistency performance of the proton exchange membrane fuel cell stack is a crucial indicator for vehicular applications. This paper comprehensively studies for the first time the local dynamic inconsistency of a high-power fuel cell stack with 30 kW rated power based on impedance. The stack is divided into ten groups equally. First, each polarization process and frequency impedance under different working conditions is quantitatively analyzed by the equivalent circuit model combined with the relaxation time distribution. It is determined that 1 Hz, 50 Hz, and 2500 Hz impedance can describe the oxygen transport, charge transfer, and ohmic variation trends, respectively. Then, the effects of loads, air stoichiometry, backpressure, operating temperature, and cathode humidity on in-stack dynamic inconsistency response are evaluated from an internal dynamics perspective. The results show that the local dynamic inconsistent responses in the fuel cell stack depend mainly on the oxygen transfer capacity. Oxygen transport affects the difference in local voltage transient drop after a current step and the inconsistency voltage recovery. These works provide an in-depth understanding of the differences in the dynamic response of high-power fuel cell stacks, which has an important implication for system control and structural design optimization.

Particle-swarm based modelling reveals two distinct classes of CRHPVN neurons.

Electrophysiological recordings can provide detailed information of single neurons' dynamical features and shed light on their response to stimuli. Unfortunately, rapidly modelling electrophysiological data for inferring network-level behaviours remains challenging. Here, we investigate how modelled single neuron dynamics leads to network-level responses in the paraventricular nucleus of the hypothalamus (PVN), a critical nucleus for the mammalian stress response. Recordings of corticotropin releasing hormone neurons from the PVN (CRHPVN ) were performed using whole-cell current-clamp. These, neurons, which initiate the endocrine response to stress, were rapidly and automatically fit to a modified adaptive exponential integrate-and-fire model (AdEx) with particle swarm optimization (PSO). All CRHPVN neurons were accurately fit by the AdEx model with PSO. Multiple sets of parameters were found that reliably reproduced current-clamp traces for any single neuron. Despite multiple solutions, the dynamical features of the models such as the rheobase, fixed points, and bifurcations, were shown to be stable across fits. We found that CRHPVN neurons can be divided into two subtypes according to their bifurcation at the onset of firing: CRHPVN -integrators and CRHPVN -resonators. The existence of CRHPVN -resonators was then directly confirmed in a follow-up patch-clamp hyperpolarization protocol which readily induced post-inhibitory rebound spiking in 33% of patched neurons. We constructed networks of CRHPVN model neurons to investigate the network level responses of CRHPVN neurons. We found that CRHPVN -resonators maintain baseline firing in networks even when all inputs are inhibitory. The dynamics of a small subset of CRHPVN neurons may be critical to maintaining a baseline firing tone in the PVN. KEY POINTS: Corticotropin-releasing hormone neurons (CRHPVN ) in the paraventricular nucleus of the hypothalamus act as the final neural controllers of the stress response. We developed a computational modelling platform that uses particle swarm optimization to rapidly and accurately fit biophysical neuron models to patched CRHPVN neurons. A model was fitted to each patched neuron without the use of dynamic clamping, or other procedures requiring sophisticated inputs and fitting algorithms. Any neuron undergoing standard current clamp step protocols for a few minutes can be fitted by this procedure The dynamical analysis of the modelled neurons shows that CRHPVN neurons come in two specific 'flavours': CRHPVN -resonators and CRHPVN -integrators. We directly confirmed the existence of these two classes of CRHPVN neurons in subsequent experiments. Network simulations show that CRHPVN -resonators are critical to retaining the baseline firing rate of the entire network of CRHPVN neurons as these cells can fire rebound spikes and bursts in the presence of strong inhibitory synaptic input.

‘Use of lipid-lowering therapy: the guidelines, the drugs or the patient?’

The current step up approach in the therapy of dyslipidemias aims to reduce the amount of LDL cholesterol below a threshold that varies according to the patient's risk category, with a pharmacological approach that sees statins as a fundamental cornerstone. Although absolutely functional in reducing cardiovascular events, this therapeutic algorithm does not yet take into consideration the innumerable phenotypic variables that we can find in dyslipidemic subjects. The ever finer understanding of the pathophysiological mechanisms underlying dyslipidemias in combination with the novelties obtained through DNA genotyping will allow, in the near future, the development of a 'tailor-made' therapy for each category of patients. This article will summarize the most recent evidence regarding the therapy of dyslipidemias, with particular attention to the concept of cumulative exposure and some hypotheses on possible initial therapeutic proposals in patients with diabetes, vasculopathy, with hypertriglyceridaemia and with high levels of Lp (a).

FLAG: A Feedback-aware Local and Global Model for Heterogeneous Sequential Recommendation

Heterogeneous sequential recommendation that models sequences of items associated with more than one type of feedback such as examinations and purchases is an emerging topic in the research community, which is also an important problem in many real-world applications. Though there are some methods proposed to exploit different types of feedback in item sequences such as RLBL, RIB, and BINN, they are based on RNN and may not be very competitive in capturing users’ complex and dynamic preferences. And most existing advanced sequential recommendation methods such as the CNN- and attention-based methods are often designed for making use of item sequences with one single type of feedback, which thus can not be applied to the studied problem directly. As a response, we propose a novel feedback-aware local and global (FLAG) preference learning model for heterogeneous sequential recommendation. Our FLAG contains four modules, including (i) a local preference learning module for capturing a user’s short-term interest, which adopts a novel feedback-aware self-attention block to distinguish different types of feedback; (ii) a global preference learning module for modeling a user’s global preference; (iii) a local intention learning module, which takes a user’s real feedback in the next step, i.e., the user’s intention at the current step, as the query vector in a self-attention block to figure out the items that match the user’s intention well; and (iv) a prediction module for preference integration and final prediction. We then conduct extensive experiments on three public datasets and find that our FLAG significantly outperforms 13 very competitive baselines in terms of two commonly used ranking-oriented metrics in most cases. We also include ablation studies and sensitivity analysis of our FLAG to have more in-depth insights.

Derivation of asthma severity from electronic prescription records using British thoracic society treatment steps

Background:Asthma severity is typically assessed through a retrospective assessment of the treatment required to control symptoms and to prevent exacerbations. The joint British Thoracic Society and Scottish Intercollegiate Guidelines Network (BTS/SIGN) guidelines encourage a stepwise approach to pharmacotherapy, and as such, current treatment step can be considered as a severity categorisation proxy. Briefly, the steps for adults can be summarised as: no controller therapy (Step 0), low-strength Inhaled Corticosteroids (ICS; Step 1), ICS plus Long-Acting Beta-2 Agonist (LABA; Step 2), medium-dose ICS + LABA (Step 3), and finally either an increase in strength or additional therapies (Step 4). This study aimed to investigate how BTS/SIGN Steps can be estimated from across a large cohort using electronic prescription records, and to describe the incidence of each BTS/SIGN Step in a general population.Methods:There were 41,433,707 prescriptions, for 671,304 individuals, in the Asthma Learning Health System Scottish cohort, between 1/2009 and 3/2017. Days on which an individual had a prescription for at least one asthma controller (preventer) medication were labelled prescription events. A rule-based algorithm was developed for extracting the strength and volume of medication instructed to be taken daily from free-text data fields. Asthma treatment regimens were categorised by the combination of medications prescribed in the 120 days preceding any prescription event and categorised into BTS/SIGN treatment steps.Results:Almost 4.5 million ALHS prescriptions were for asthma controllers. 26% of prescription events had no inhaled corticosteroid prescriptions in the preceding 120 days (Step 0), 16% were assigned to BTS/SIGN Step 1, 7% to Step 2, 21% to Step 3, and 30% to Step 4. The median days spent on a treatment step before a step-down in treatment was 297 days, whereas a step-up only took a median of 134 days.ConclusionWe developed a reproducible methodology enabling researchers to estimate BTS/SIGN asthma treatment steps in population health studies, providing valuable insights into population and patient-specific trajectories, towards improving the management of asthma.

How Do Implicit Bias and Stereotype Threat Affect Mental Health Care for a Student of Color?

On Being a PatientNovember 2022How Do Implicit Bias and Stereotype Threat Affect Mental Health Care for a Student of Color?Chukwuemeka N. Uwakaneme, BAChukwuemeka N. Uwakaneme, BAVirginia Tech Carilion School of Medicine, Roanoke, VirginiaSearch for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M22-0934 Audio Reading - “How Do Implicit Bias and Stereotype Threat Affect Mental Health Care for a Student of Color?” Audio. Michael A. Lacombe, MD, Annals Associate Editor, reads “How Do Implicit Bias and Stereotype Threat Affect Mental Health Care for a Student of Color?” by Chukwuemeka N. Uwakaneme, BA Your browser does not support the audio element. Audio player progress bar Step backward in current audio track Play current audio trackPause current audio track Step forward in current audio track Mute current audio trackUnmute current audio track 00:00/ SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail The other day, I was listening to a popular storytelling and journalism podcast called This American Life, which bases its episodes on themes. This episode's theme dealt with people being on television when they didn't necessarily want to be.In the episode, a television crew for the show Cops is filming a traffic stop. The police officer approaches two silent teenagers and asks to search their car. They agree immediately, although you can't really hear them. The officer finds what he believes to be crack cocaine in the back seat and does what they call a Narcotics Identification Kit test. ... Author, Article, and Disclosure InformationAffiliations: Virginia Tech Carilion School of Medicine, Roanoke, VirginiaCorresponding Author: Chukwuemeka N. Uwakaneme, BA, 2 Riverside Circle, Roanoke, VA, 24016; e-mail, [email protected]edu. PreviousarticleNextarticle Advertisement Audio Reading - “How Do Implicit Bias and Stereotype Threat Affect Mental Health Care for a Student of Color?” Audio. Michael A. Lacombe, MD, Annals Associate Editor, reads “How Do Implicit Bias and Stereotype Threat Affect Mental Health Care for a Student of Color?” by Chukwuemeka N. Uwakaneme, BA Your browser does not support the audio element. Audio player progress bar Step backward in current audio track Play current audio trackPause current audio track Step forward in current audio track Mute current audio trackUnmute current audio track 00:00/ FiguresReferencesRelatedDetails Metrics November 2022Volume 175, Issue 11Page: 1619-1620 ePublished: 15 November 2022 Issue Published: November 2022 Copyright & PermissionsCopyright © 2022 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...

Lost monarchs

Ad LibitumNovember 2022lost monarchsD.A. Frater, MDD.A. Frater, MDE. Barrow Medical Group Dallas, TexasSearch for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M21-4559 Audio Reading - “lost monarchs” Audio. Michael A. Lacombe, MD, Annals Associate Editor, reads “lost monarchs” by D.A. Frater, MD Your browser does not support the audio element. Audio player progress bar Step backward in current audio track Play current audio trackPause current audio track Step forward in current audio track Mute current audio trackUnmute current audio track 00:00/ SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail this fall I sat in quiet wake abovean unclean creek that lured me once from classto stand barefoot like a beakless heronstalking crayfish without fear in its thenclear waters of glass before I was stoppedstood tall by the sight of them Monarchs hundredsfluttering like autumn leaves who've shedtheir sinewy tethers to help us recallnature's unsurpassing beauty to mindan unseen path to their wintering wherenow in the fading clutch of memoryjust a few still find the unquiettrees whose whispers warned of a timewhen the air would be dead and nonewould recall its passing Author, Article, and Disclosure InformationAffiliations: E. Barrow Medical Group Dallas, TexasCorresponding Author: D.A. Frater, MD, E. Barrow Medical Group, 8230 Walnut Hill Lane, Suite 620, Dallas, TX 75231; e-mail, [email protected]com. PreviousarticleNextarticle Advertisement Audio Reading - “lost monarchs” Audio. Michael A. Lacombe, MD, Annals Associate Editor, reads “lost monarchs” by D.A. Frater, MD Your browser does not support the audio element. Audio player progress bar Step backward in current audio track Play current audio trackPause current audio track Step forward in current audio track Mute current audio trackUnmute current audio track 00:00/ FiguresReferencesRelatedDetails Metrics November 2022Volume 175, Issue 11Page: 1616 ePublished: 15 November 2022 Issue Published: November 2022 Copyright & PermissionsCopyright © 2022 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...

A comprehensive heat generation study of lithium titanate oxide-based lithium-ion batteries

A precise interpretation of lithium-ion battery (LIB) heat generation is indispensable to the advancement and accomplishment of thermal management systems for different applications of LIB, including electric vehicles. The internal resistance of a lithium titanate oxide (LTO)-based LIB was determined at different state of charge (SOC) levels and current rates to understand the relationship between internal resistance and heat generation. Random and different pulse discharge current step durations were applied to consider the effect of different SOC interval levels on heat generation. The total generated heat was measured for different discharge rates and operating temperatures in a Netzsch IBC 284 calorimeter. It was seen that a 6.7% SOC decrease at high SOC levels corresponds to 0.377 Wh, 0.728 Wh, and 1.002 Wh heat generation for 26A, 52A, and 78A step discharge, both at 20 °C and 30 °C. However, a 1.85% SOC decrease at medium SOC levels corresponds already to 0.57 Wh, 0.76 Wh, and 0.62 Wh heat generation. It can be inferred that the impact of SOC level on heat generation for this cell is more prominent at a lower than at a higher SOC.