The Problem of Large-N Phase Transition in Kazakov-Migdal Model of Induced QCD

We study the lattice gauge model proposed recently by Kazakov and Migdal for inducing QCD. We discuss an extra local Z_N which is a symmetry of the model and propose of how to construct observables. We discuss the role of the large-N phase transition which should occur before the one associated with the continuum limit in order that the model describes continuum QCD. We formulate the mean field approach to study the large-N phase transition for an arbitrary potential and show that no first order phase transition occurs for the quadratic potential.Comment: 10 pages, ITEP-YM-5-9

Do environmental regulations and technological innovation enhance environmental well‐being in sub‐Saharan Africa?

We investigate the regulation–technology–environment nexus in sub‐Saharan Africa (SSA), one of the world's most rapidly growing regions. Using a comprehensive panel dataset consisting of 32 countries from 2000 to 2022, we find that stronger environmental regulations and technological innovation enhance environmental well‐being. Moreover, we identify that stronger environmental regulations positively affect pro‐environment innovation. Finally, we present clear evidence for a dynamic and nonlinear regulation–technology–environment relationship, ruling out one‐size‐fits‐all policy approaches to environmental well‐being. Our results remain robust to different estimators, measurements, and sample selections

An impedance model for analysis of EIS of polymer electrolyte fuel cells under hydrogen peroxide formation in the cathode

Abstract In this study, an impedance model based on electrochemical theory considering hydrogen peroxide formation during a two-step oxygen reduction reaction (ORR) in polymer electrolyte fuel cells (PEFCs) has been developed. To validate the theoretical treatment, electrochemical impedance spectroscopy (EIS) measurements were carried out in an open-cathode 16 cm2 H2/air PEFC stack. The results show that inductive loops at low frequencies of the impedance spectra are attributed to mechanisms related to hydrogen peroxide formation during ORR. The results also demonstrate that the mechanisms during consumption of hydrogen peroxide to form water (second-step in ORR) can be the dominating process for losses in the PEFC compared to the mechanisms during oxygen consumption to form hydrogen peroxide (first-step in ORR). Oxygen transport limitations can be a result of hydrogen peroxide adsorbed onto the surface of the electrode which reduces the number of active sites in the cathode catalyst layer for oxygen to react. This study could support results from other experimental techniques to identify hydrogen peroxide formation during the ORR that limit the performance of PEFCs

Evaluate the validity of electrochemical impedance measurements of polymer electrolyte fuel cells using a computational algorithm based on Fast Fourier Transform

The validity of electrochemical impedance measurements of polymer electrolyte fuel cells (PEFCs) have to be evaluated before an attempt is made to interpret the electrochemical mechanisms represented in the Nyquist plot. This evaluation can be carried out by data transformation of impedance measurements using Kramers-Kronig (K-K) relations. However, this evaluation has been commonly neglected in the fuel cell area due to the complexity of applying the mathematical K-K relations to real-world impedance measurements. In this study a computational algorithm, based on the Fast Fourier Transform (FFT) theory, the Hilbert transformation of impedance data, and a validated impedance model for PEFCs, for evaluating data transformation (real to imaginary Z’→Z’’ and imaginary to real Z’’→Z’) and hence validity of impedance measurements of PEFCs has been developed in Matlab®. With this computational algorithm it is possible to identify the factors that lead to incorrect EIS measurements of PEFCs such as inductance effect from the electrical cables of the measurement system, incorrect AC amplitude signal, and instability during EIS measurements. The computational algorithm developed in this study enables more accurate impedance results to be obtained to study the performance and state of health of PEFCs

Steps in the bacterial flagellar motor

The bacterial flagellar motor is a highly efficient rotary machine used by many bacteria to propel themselves. It has recently been shown that at low speeds its rotation proceeds in steps [Sowa et al. (2005) Nature 437, 916--919]. Here we propose a simple physical model that accounts for this stepping behavior as a random walk in a tilted corrugated potential that combines torque and contact forces. We argue that the absolute angular position of the rotor is crucial for understanding step properties, and show this hypothesis to be consistent with the available data, in particular the observation that backward steps are smaller on average than forward steps. Our model also predicts a sublinear torque-speed relationship at low torque, and a peak in rotor diffusion as a function of torque

A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques

Measurement of CP observables for the decays B^± → D^0_(CP)K^±

We present a study of the decay B^- → D^0_(CP)K^± and its charge conjugate, where D^0_CP) is reconstructed in CP-even, CP-odd, and non-CP flavor eigenstates, based on a sample of 232 x 10^6 Y(4S) → BB decays collected with the BABAR detector at the PEP-II e^+e^- storage ring. We measure the partial-rate charge asymmetries A_(CP±) and the ratios R_(CP±) of the B → D^0K decay branching fractions as measured in CP^± and non-CP D^0 decays: A_(CP±) 0:35 ± 0.13(stat) ± 0.04(syst), A_(CP-)= -0.06 ± 0.13(stat) ± 0.04(syst), R_(CP+) = 0.90 ± 0.12(stat) ± 0.049syst), and R_(CP-) = 0:86 ± 0.10(stat) ± 0.05(syst)

Identification of a Novel Invasion-Promoting Region in Insulin Receptor Substrate 2

Although the insulin receptor substrate (IRS) proteins IRS1 and IRS2 share considerable homology and activate common signaling pathways, their contributions to breast cancer are distinct. IRS1 has been implicated in the proliferation and survival of breast tumor cells. In contrast, IRS2 facilitates glycolysis, invasion, and metastasis. To determine the mechanistic basis for IRS2-dependent functions, we investigated unique structural features of IRS2 that are required for invasion. Our studies revealed that the ability of IRS2 to promote invasion is dependent upon upstream insulin-like growth factor 1 receptor (IGF-1R)/insulin receptor (IR) activation and the recruitment and activation of phosphatidylinositol 3-kinase (PI3K), functions shared with IRS1. In addition, a 174-amino-acid region in the IRS2 C-terminal tail, which is not conserved in IRS1, is also required for IRS2-mediated invasion. Importantly, this invasion (INV) region is sufficient to confer invasion-promoting ability when swapped into IRS1. However, the INV region is not required for the IRS2-dependent regulation of glucose uptake. Bone morphogenetic protein 2-inducible kinase (BMP2K) binds to the INV region and contributes to IRS2-dependent invasion. Taken together, our data advance the mechanistic understanding of how IRS2 regulates invasion and reveal that IRS2 functions important for cancer can be independently targeted without interfering with the metabolic activities of this adaptor protein