Doping evolution of antiferromagnetism and transport properties in the non-superconducting BaFe2-2xNixCrxAs2

We report elastic neutron scattering and transport measurements on the Ni and Cr equivalently doped iron pnictide BaFe$_{2-2x}$Ni$_{x}$Cr$_{x}$As$_{2}$. Compared with the electron-doped BaFe$_{2-x}$Ni$_{x}$As$_{2}$, the long-range antiferromagnetic (AF) order in BaFe$_{2-2x}$Ni$_{x}$Cr$_{x}$As$_{2}$ is gradually suppressed with vanishing ordered moment and N\'{e}el temperature near $x= 0.20$ without the appearance of superconductivity. A detailed analysis on the transport properties of BaFe$_{2-x}$Ni$_{x}$As and BaFe$_{2-2x}$Ni$_{x}$Cr$_{x}$As$_{2}$ suggests that the non-Fermi-liquid behavior associated with the linear resistivity as a function of temperature may not correspond to the disappearance of the static AF order. From the temperature dependence of the resistivity in overdoped compounds without static AF order, we find that the transport properties are actually affected by Cr impurity scattering, which may induce a metal-to-insulator crossover in highly doped BaFe$_{1.7-y}$Ni$_{0.3}$Cr$_{y}$As$_{2}$.Comment: 10 pages, 12 figure

Electron doping evolution of the magnetic excitations in BaFe2-xNixAs2

We use inelastic neutron scattering (INS) spectroscopy to study the magnetic excitations spectra throughout the Brioullion zone in electron-doped iron pnictide superconductors BaFe$_{2-x}$Ni$_{x}$As$_{2}$ with $x=0.096,0.15,0.18$. While the $x=0.096$ sample is near optimal superconductivity with $T_c=20$ K and has coexisting static incommensurate magnetic order, the $x=0.15,0.18$ samples are electron-overdoped with reduced $T_c$ of 14 K and 8 K, respectively, and have no static antiferromagnetic (AF) order. In previous INS work on undoped ($x=0$) and electron optimally doped ($x=0.1$) samples, the effect of electron-doping was found to modify spin waves in the parent compound BaFe$_2$As$_2$ below $\sim$100 meV and induce a neutron spin resonance at the commensurate AF ordering wave vector that couples with superconductivity. While the new data collected on the $x=0.096$ sample confirms the overall features of the earlier work, our careful temperature dependent study of the resonance reveals that the resonance suddenly changes its $Q$-width below $T_c$ similar to that of the optimally hole-doped iron pnictides Ba$_{0.67}$K$_{0.33}$Fe$_2$As$_2$. In addition, we establish the dispersion of the resonance and find it to change from commensurate to transversely incommensurate with increasing energy. Upon further electron-doping to overdoped iron pnictides with $x=0.15$ and 0.18, the resonance becomes weaker and transversely incommensurate at all energies, while spin excitations above $\sim$100 meV are still not much affected. Our absolute spin excitation intensity measurements throughout the Brillouin zone for $x=0.096,0.15,0.18$ confirm the notion that the low-energy spin excitation coupling with itinerant electron is important for superconductivity in these materials, even though the high-energy spin excitations are weakly doping dependent.Comment: 16 pages, 16 figure

A MIC-LSTM based parameter extraction method for single-diode PV model

In recent years, the installed capacity of renewable energy systems has seen rapid growth, particularly in photovoltaic (PV) power. Photovoltaic modules, being the fundamental elements of the PV system, play a crucial role in determining system performance. However, the challenge arises from the inconsistent decay rates of PV modules, which significantly impact the accuracy of PV system modeling. To address this issue, this paper introduces a novel MIC-LSTM based parameter extraction method for the single-diode PV model. This method focuses on accurately deriving PV module model parameters under various decay rates. By establishing a mapping relationship between the current-voltage (I-V) curve characteristics and the five unknown parameters in the photovoltaic module model, the proposed method demonstrates high precision in parameter extraction. Simulation and experimental verifications are carried out to validate the proposed method, where the extraction accuracy is 99.3%, 98.39%, 98.85%, 97.91%, and 98.36% for the five unknown model parameters

Precursory Cooper Flow in Ultralow-Temperature Superconductors

Superconductivity at low temperature -- observed in lithium and bismuth, as well as in various low-density superconductors -- calls for developing reliable theoretical and experimental tools for predicting ultralow critical temperatures, $T_c$, of Cooper instability in a system demonstrating nothing but normal Fermi liquid behavior in a broad range of temperatures below the Fermi energy, $T_{\rm F}$. Equally important are controlled predictions of stability in a given Cooper channel. We identify such a protocol within the paradigm of precursory Cooper flow -- a universal ansatz describing logarithmically slow temperature evolution of the linear response of the normal state to the pair-creating perturbation. Applying this framework to the two-dimensional uniform electron gas, we reveal a series of exotic superconducting states, pushing controlled theoretical predictions of $T_c$ to the unprecedentedly low scale of $10^{-100} T_{\rm F}$

Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies

OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications. Despite its promise, recent studies and internal reviews highlight its underperformance in specialized medical tasks. This paper explores the boundary of GPT-4V's capabilities in medicine, particularly in processing complex imaging data from endoscopies, CT scans, and MRIs etc. Leveraging open-source datasets, we assessed its foundational competencies, identifying substantial areas for enhancement. Our research emphasizes prompt engineering, an often-underutilized strategy for improving AI responsiveness. Through iterative testing, we refined the model's prompts, significantly improving its interpretative accuracy and relevance in medical imaging. From our comprehensive evaluations, we distilled 10 effective prompt engineering techniques, each fortifying GPT-4V's medical acumen. These methodical enhancements facilitate more reliable, precise, and clinically valuable insights from GPT-4V, advancing its operability in critical healthcare environments. Our findings are pivotal for those employing AI in medicine, providing clear, actionable guidance on harnessing GPT-4V's full diagnostic potential

Label-free timing analysis of modularized nuclear detectors with physics-constrained deep learning

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized. In this paper, we propose a novel method based on deep learning for timing analysis of modularized nuclear detectors without explicit needs of labelling event data. By taking advantage of the inner time correlation of individual detectors, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on two experimental datasets. In the toy experiment, the neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. In the electromagnetic calorimeter experiment, several neural network models (FC, CNN and LSTM) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely.Comment: 25 pages, 10 figure

Low dose triptolide reverses chemoresistance in adult acute lymphoblastic leukemia cells via reactive oxygen species generation and DNA damage response disruption

Chemoresistance represents a major challenge for treatment of acute lymphoblastic leukemia (ALL). Thus, new drugs to overcome chemoresistance in ALL are urgently needed. To this end, we established a cytarabine (araC)-resistant ALL cell line (NALM-6/R), which interestingly displayed cross-resistance towards doxorubicin (ADM). Here we report that low dose of triptolide (TPL), a natural product used for treating inflammatory diseases such as arthritis, could reverse araC and ADM resistance and in NALM-6/R cells as well as primary cells from patients with relapsed or refractory (R/R) ALL, reflected by inhibition of cell proliferation and induction of apoptosis in vitro, and repression of tumor growth in vivo in a mouse xenograft model. Mechanistically, these events were associated with impaired mitochondrial membrane potential and increased reactive oxygen species (ROS) production. Co-treatment with TPL and araC or ADM upregulated pro-apoptotic caspase-9 protein, inhibited checkpoint kinase 1 (Chk1) and 2 (Chk2) phosphorylation, and induced γH2A.X (a DNA damage marker). Notably, the combination regimen of TPL and conventional chemotherapeutics also rapidly diminished tumor burden in a patient with R/R ALL. Together, these findings provide preclinical evidence for repurposing use of TPL in combination with chemotherapeutic agents to treat R/R ALL as an alternative salvage regimen