On the Smarandache LCM dual function

The main purpose of this paper is using the elementary method to study the calculating problem of a Dirichlet series involving the Smarandache LCM dual function SL*(n) and the mean value distribution property of SL*(n), obtain an exact calculating formula and a sharper asymptotic formula for it

The Correspondence between Convergence Peaks from Weak Lensing and Massive Dark Matter Haloes

The convergence peaks, constructed from galaxy shape measurement in weak lensing, is a powerful probe of cosmology as the peaks can be connected with the underlined dark matter haloes. However the capability of convergence peak statistic is affected by the noise in galaxy shape measurement, signal to noise ratio as well as the contribution from the projected mass distribution from the large-scale structures along the line of sight (LOS). In this paper we use the ray-tracing simulation on a curved sky to investigate the correspondence between the convergence peak and the dark matter haloes at the LOS. We find that, in case of no noise and for source galaxies at $z_{\rm s}=1$, more than $65\%$ peaks with $\text{SNR} \geq 3$ (signal to noise ratio) are related to more than one massive haloes with mass larger than $10^{13} {\rm M}_{\odot}$. Those massive haloes contribute $87.2\%$ to high peaks ($\text{SNR} \geq 5$) with the remaining contributions are from the large-scale structures. On the other hand, the peaks distribution is skewed by the noise in galaxy shape measurement, especially for lower SNR peaks. In the noisy field where the shape noise is modelled as a Gaussian distribution, about $60\%$ high peaks ($\text{SNR} \geq 5$) are true peaks and the fraction decreases to $20\%$ for lower peaks ($3 \leq \text{SNR} < 5$). Furthermore, we find that high peaks ($\text{SNR} \geq 5$) are dominated by very massive haloes larger than $10^{14} {\rm M}_{\odot}$.Comment: 13 pages, 11 figures, 4 tables, accepted for publication in MNRAS. Our mock galaxy catalog is available upon request by email to the author ([email protected]

Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation

BackgroundDisulfidptosis is a newly identified variant of cell death characterized by disulfide accumulation, which is independent of ATP depletion. Accordingly, the latent influence of disulfidptosis on the prognosis of lung adenocarcinoma (LUAD) patients and the progression of tumors remains poorly understood.MethodsWe conducted a multifaceted analysis of the transcriptional and genetic modifications in disulfidptosis regulators (DRs) specific to LUAD, followed by an evaluation of their expression configurations to define DR clusters. Harnessing the differentially expressed genes (DEGs) identified from these clusters, we formulated an optimal predictive model by amalgamating 10 distinct machine learning algorithms across 101 unique combinations to compute the disulfidptosis score (DS). Patients were subsequently stratified into high and low DS cohorts based on median DS values. We then performed an exhaustive comparison between these cohorts, focusing on somatic mutations, clinical attributes, tumor microenvironment, and treatment responsiveness. Finally, we empirically validated the biological implications of a critical gene, KYNU, through assays in LUAD cell lines.ResultsWe identified two DR clusters and there were great differences in overall survival (OS) and tumor microenvironment. We selected the "Least Absolute Shrinkage and Selection Operator (LASSO) + Random Survival Forest (RFS)" algorithm to develop a DS based on the average C-index across different cohorts. Our model effectively stratified LUAD patients into high- and low-DS subgroups, with this latter demonstrating superior OS, a reduced mutational landscape, enhanced immune status, and increased sensitivity to immunotherapy. Notably, the predictive accuracy of DS outperformed the published LUAD signature and clinical features. Finally, we validated the DS expression using clinical samples and found that inhibiting KYNU suppressed LUAD cells proliferation, invasiveness, and migration in vitro.ConclusionsThe DR-based scoring system that we developed enabled accurate prognostic stratification of LUAD patients and provides important insights into the molecular mechanisms and treatment strategies for LUAD

State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model

State of charge (SOC) is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX) model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC) cycles are implemented on the 60AH LiFePO4 battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs

Field programmable gate array (FPGA) application and future development

With the application of cloud computing,big data and AI technology, CPU alone can no longer meet the computing power needs of all walks of life. At present, the two most widely used acceleration components in AI computing platform are GPU and FPGA, but recently FPGA has been frequently favored by various AI giants, such as Microsoft, Baidu are looking forward to the future of FPGA applications

Training feed-forward neural networks using the gradient descent method with the optimal stepsize

The most widely used algorithm for training multiplayer feedforward networks, Error BackPropagation (EBP), is an iterative gradient descend algorithm by nature. Variable stepsize is the key to fast convergence of BP networks. A new optimal stepsize algorithm is proposed for accelerating the training process. It modifies the objective function to reduce the computational complexity of the Jacobin and consequently that of Hessian matrices, and hereby directly computes the optimal iterative stepsize. The improved backpropagation algorithm helps alleviating the problem of slow convergence and oscillations. The analysis indicates that the backpropagation with optimal stepsize (BPOS) is more efficient when treating large-scale samples. The numerical experiment results on pattern recognition and function approximation problems show that the proposed algorithm possesses the features of fast convergence and less intensive computational complexity.Godkänd; 2012; 20120420 (andbra

Interurban Consumption Flows of Urban Agglomeration in the Middle Reaches of the Yangtze River: A Network Approach

Network analysis through the lens of traffic flows is becoming the focus of urban-region research in the context of globalization. This study examines the network relationship among 31 cities in the middle reaches of the Yangtze River from the perspective of consumption flow that is serving as an increasingly important engine for China&rsquo;s growth in general and regional momentum in particular. By tracing the directions and magnitudes of bank card transactions in 2016 as provided by Chinese UnionPay, this paper finds: (1) Three capital cities dominate both outward and inward flow networks and have net outflows, whereas others are featured with inward consumption flow; (2) Most interurban flows occur within provincial boundary, which leaves this whole region without a city-generating global impact; (3) Eight sub-networks composed of adjoining few cities can be further identified that have closer connections to the one containing capital city in that province. Based on these findings related to boundary effect, this paper concludes with a vision for more integrated consumption networks in the context of this region