Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on the Design of Error-Based Adaptive Robust RBF Neural Network Back-Stepping Controller for 2-DOF Snake Robot’s Head

In this paper, we will propose the controller of a 2-DOF head in a snake robot for effective image data reading when the snake robot is driving, and present an error-based adaptive robust radial base function neural network back-stepping controller. The snake robot head system is a nonlinear system, and there are unmeasurable disturbances that occur while driving. To solve this problem, we use back-stepping controller and radial basis function neural network to compensate for unmeasurable disturbances to improve the steady state. In order to further compensate for the network approximation error that occurs during training or large signal changes, we use adaptive coefficients and error functions to approximate the network approximation error and design adaptive robust terms. Compared to the previous controller, the proposed controller can actively compensate for large signal changes and has the advantage of not generating residual input in a steady state. The proposed controller is based on Lyapunov function candidate to design an adaptive law and prove the system stability. The stability of the control system is proven through Lyapunov analysis and bounded. The proposed controller compared and verified the controller performance for two inputs through simulation and presented the efficiency of the controller.
Safety and efficacy of radiotherapy/chemoradiotherapy combined with immune checkpoint inhibitors for non-small cell lung cancer: A systematic review and meta-analysis

Background It is now widely accepted that radiotherapy (RT) can provoke a systemic immune response, which gives a strong rationale for the combination of RT and immune checkpoint inhibitors (ICIs). However, RT is a double-edged sword that not only enhances systemic antitumor immune response, but also promotes immunosuppression to some extent. Nevertheless, many aspects regarding the efficacy and safety of this combination therapy remain unknown. Therefore, a systematic review and meta-analysis was performed in order to assess the safety and efficacy of RT/chemoradiotherapy (CRT) and ICI combination therapy for non-small cell lung cancer (NSCLC) patients. Methods PubMed and several other databases were searched (according to specific criteria) to find relevant studies published prior to the 28th of February 2022. Results 3,652 articles were identified for screening and 25 trials containing 1,645 NSCLC patients were identified. For stage II-III NSCLC, the one- and two-year overall survival (OS) was 83.25% (95% confidence interval (CI): 79.42%-86.75%) and 66.16% (95% CI: 62.3%-69.92%), respectively. For stage IV NSCLC, the one- and two-year OS was 50% and 25%. In our study, the pooled rate of grade 3-5 adverse events (AEs) and grade 5 AEs was 30.18% (95% CI: 10.04%-50.33%, I2: 96.7%) and 2.03% (95% CI: 0.03%-4.04%, I2: 36.8%), respectively. Fatigue (50.97%), dyspnea (46.06%), dysphagia (10%-82.5%), leucopenia (47.6%), anaemia (5%-47.6%), cough (40.09%), esophagitis (38.51%), fever (32.5%-38.1%), neutropenia (12.5%-38.1%), alopecia (35%), nausea (30.51%) and pneumonitis (28.53%) were the most common adverse events for the combined treatment. The incidence of cardiotoxicity (0%-5.00%) was low, but it was associated with a high mortality rate (0%-2.56%). Furthermore, the incidence of pneumonitis was 28.53% (95% CI: 19.22%-38.88%, I2: 92.00%), grade ≥ 3 pneumonitis was 5.82% (95% CI: 3.75%-8.32%, I2: 57.90%) and grade 5 was 0%-4.76%. Conclusion This study suggests that the addition of ICIs to RT/CRT for NSCLC patients may be both safe and feasible. We also summarize details of different RT combinations with ICIs to treat NSCLC. These findings may help guide the design of future trials, the testing of concurrent or sequential combinations for ICIs and RT/CRT could be particularly useful to guide the treatment of NSCLC patients.
An SQP Algorithm for Optimization with Linear Complementary Constraints

Against the shortcomings that many existing algorithms for solving the standard smoothing nonlinear programming( SSNP) would fail if they were used directly to solve the mathematical programs with equilibrium constraints( MPEC). By using a complementarity function and the idea of smoothing approximation method,the MPEC problem was transformed into a nonlinear programming,and an SQP algorithm is proposed for the solution of MPEC problem. Further the numerical solution to the algorithm is given. Preliminary numerical results show that it can be a very good convergence speed and optimization results.
Mining High Utility Itemsets Using Prefix Trees and Utility Vectors

High utility itemsets can reveal combinations of items that have a high profit, expense, or importance. Mining high utility itemsets in a database with <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="qu-ieq1-3256126.gif"/></alternatives></inline-formula> items generally results in a huge search space, composed of <inline-formula><tex-math notation="LaTeX">$2^{n}$</tex-math><alternatives><mml:math><mml:msup><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:msup></mml:math><inline-graphic xlink:href="qu-ieq2-3256126.gif"/></alternatives></inline-formula> itemsets, and heavy utility calculations for the explored itemsets. Previous algorithms using prefix tree structures perform two phases, namely candidate generation and testing. To avoid generating candidate itemsets, one-phase algorithms use list or hyper-link structures and have been proven to be superior to two-phase algorithms. However, it should be noted that a prefix tree is still an efficient structure for itemset mining problems, and especially algorithms using prefix trees such as FP-Growth have shown excellent performance for mining frequent itemsets. This paper proposes Hamm, a High-performance AlgorithM for Mining high utility itemsets. Hamm employs a novel TV (prefix Tree and utility Vector) structure and mines high utility itemsets in one phase without candidate generation. We also develop an efficient optimization which is incorporated into Hamm as a component. Using prefix trees and utility vectors, Hamm outperforms state-of-the-art algorithms on various databases in experiments. Experimental results also show that the proposed optimization remarkably reduces the search space and speeds up Hamm.
Campaign capability evaluation of weapon systems based on fuzzy sets and rough sets

To solve the problem that weight of each attribute is usually determined subjectively in multiattribute decision-making,a new model based on fuzzy sets and rough sets is proposed to assess the campaign capability of weapon systems.Sample values having been normalized,each sample is ranked by a fuzzy partial rank model.A decision-making information list of continuous values is built so as to solve the problem that there must be a decision-making information list in general rough sets.Then the weight coefficient of each attribute value is calculated based on generalized importance of attribute to avoid the bad aftereffect worked by dispersing attribute values sensitized to noise.Combining with an air combat ability assessment of fighters,the feasibility and validity of weapon systems' campaign capability assessment by this method are verified.
Depthwise Temporal Non-Local Network for Faster and Better Dynamic Hand Gesture Authentication

Dynamic hand gesture is an emerging and promising biometric trait. It contains both physiological and behavioral characteristics, which on the one hand can theoretically make authentication systems more accurate and more secure, and on the other hand can increase the difficulty of model design because it is essentially a fine-grained video understanding task. For authentication systems, equal error rate (EER) and real-time performance are two vital metrics. Current video understanding-based hand gesture authentication methods mainly focus on lowering the EER while neglecting to reduce the computational cost. In this paper, we propose a 2D CNN-based depthwise temporal non-local network (DwTNL-Net) that can take into account both EER and running efficiency. To enable the DwTNL-Net with spatiotemporal information processing capability, we design a temporal sharpening (TS) module and a DwTNL module for short- and long-term identity feature modeling, respectively. The TS module can assist the backbone in local behavioral characteristic understanding and can simultaneously reduce redundant information and highlight behavioral cues while retaining sufficient physiological characteristics. In contrast, the DwTNL module focuses on summarizing global information and discovering stable patterns, which are finally used for local information enhancement. The complementary combination of our TS and DwTNL modules makes DwTNL-Net achieve substantial performance improvements. Extensive experiments on the SCUT-DHGA dataset and sufficient statistical analyses fully demonstrate the superiority and efficiency of our DwTNL-Net. The code is available at https://github.com/SCUT-BIP-Lab/DwTNL-Net.
TMBIM6-mediated miR-181a expression regulates breast cancer cell migration and invasion via the MAPK/ERK signaling pathway

Transmembrane Bax Inhibitor Motif-containing 6 (TMBIM6) has been reported to regulate cell death pathways and is overexpressed in several types of cancers. In this study, we investigated whether high expression of TMBIM6 in breast cancer was significantly associated with cancer invasiveness. Knockdown of TMBIM6 reduced proliferation and migration of invasive breast cancer cells through downregulation of the MAPK/ERK signaling pathway. Moreover, we suggested that expression of miR-181a was significantly suppressed upon TMBIM6 knockdown. In contrast, overexpression of TMBIM6 significantly increased cell invasion and migration through up-regulation of mesenchymal markers and matrix metalloproteinase-9 (MMP-9) and enhanced activation of the MAPK/ERK signaling pathway. We also observed that up-regulation of TMBIM6 significantly increased the expression of miR-181a by TMBIM6-mediated pathway. TMBIM6 and miR-181a-mediated ERK activation induced the expression of Snail-1 and Snail-2 in FOSL-1/C-JUN-dependent manner. Overall, our data demonstrated that TMBIM6-induced miR-181a up-regulation plays an important role in the efficient modulation of migration and invasion of breast cancer cells.
Classification of brain tumor using a multistage approach based on RELM and MLBP

INTRODUCTION: Automatic segmentation and classification of brain tumors help in improvement of treatment which will increase the life of the patient. Tumor may be noncancerous (benign) or cancerous (malignant). Precancerous cells may also form into cancer.OBJECTIVES: Hough CNN is applied for selected section which applies hough casting technique in segmentation. METHODS: A multistage methodof extracting features, with multistage neighbouring is done for emerging an exact brain tumor classifying methodology.RESULTS: In this dataset three types of brain tumors are available they are meningioma, glioma, and pituitary. CONCLUSION: This paperpresented an efficient brain tumor classification approach which involves multiscale preprocessing, multiscale feature extraction and classification.
Transient analysis of cage-induction motors using finite-elements

A method for the analysis of induction motor transients using the two-dimensional finite-element method is described. Unlike previously published work the method does not use an eddy-current formulation to account for the currents induced in the rotor bars. It relies on the decomposition of the rotor currents into harmonic distributions and the use of auxiliary circuit equations to take the time-stepping into account. The adoption of this technique yields some distinct advantages. Firstly, time-stepping is limited to the circuit model alone, thereby allowing an optimum step size to be selected at all times. Secondly, the use of a magnetostatic finite-element formulation makes it possible to use nonlinear solutions of comparatively low accuracy, thus bringing substantial savings in computer resources. Finally, the extensive use of circuit techniques also allows the effect of axial skew and spatially distributed airgap flux harmonics to be incorporated. >
Asynchronous Suboptimal Model Predictive Control for Piecewise Homogeneous Markov Jump Systems

This article is devoted to the problem of the suboptimal model predictive control for hidden Markov jump systems (HMJSs) with piecewise homogeneous transition probability, which is a class of time-varying transition probabilities. The HMJS under consideration can accurately describe the asynchronous phenomena between the observed mode and the actual one. An observed-mode-dependent MPC approach with recursive feasibility and mean-square stability is designed. Then, the corresponding algorithms are designed, of which the offline algorithm has shown the same control performance and superior computational efficiency as the online one. Finally, some numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.
Impulsively started flow around a circular cylinder by the vortex method

Impulsively started, viscous, incompressible flows around a circular cylinder are simulated by a Lagrangian vortex solution of the vorticity equation using random walks for diffusion and the vortex-in-cell method for convection in a fractional-step scheme. Vortices are introduced around the surface at each timestep to satisfy the zero-slip condition. In the range of Reynolds numbers 2.5 × 102 to 105, comparisons with two analytical solutions, valid for small times (t < 1), show reasonable agreement. For somewhat longer times (t < 5), for a similar range of Reynolds numbers, comparisons are made with accurate Eulerian numerical solutions and with careful flow-visualization experiments. Agreement is good provided a sufficiently large number of vortices is introduced per timestep. The number required increases as Reynolds number increases. If too few are introduced, the vorticity in the wake tends to roll up too tightly. The vortex method remains stable, whereas Eulerian schemes have been reported to become eventually unstable unless upwind differencing is used, reducing accuracy.
Technical Note---Nonlinear Least Squares Estimation of New Product Diffusion Models

Schmittlein and Mahajan Schmittlein, D. C. , V. Mahajan. 1982. Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Marketing Sci.1 Winter 57--78. made an important improvement in the estimation of the Bass Bass, F. M. 1969. A new product growth model for consumer durables. Management Sci.15 January 215--227. diffusion model by appropriately aggregating the continuous time model over the time intervals represented by the data. However, by restricting consideration to only sampling errors and ignoring all other errors such as the effects of excluded marketing variables, their Maximum Likelihood Estimation MLE seriously underestimates the standard errors of the estimated parameters. This note uses an additive error term to model sampling and other errors in the Schmittlein and Mahajan formulation. The proposed Nonlinear Least Squares NLS approach produces valid standard error estimates. The fit and the predictive validity are roughly comparable for the two approaches. Although the empirical applications reported in this paper are in the context of the Bass diffusion model, the NLS approach is also applicable to other diffusion models for which cumulative adoption can be expressed as an explicit function of time.
Fingerprint Distribution Features in the Population of Adjara (Georgia)

Our research study interest was to analyze the quantitative specific characteristic of primaryfingerprints in the secondary and high school students in the population of Adjara. Data from 210 individuals have been studied. For obtaining the proper image of the fingerprint pattern, classical method was used. For one of the purposes of our study, the data has been processed according to all ten fingers (n = 2100), where the frequency of distribution of each fingerprint was determined. The Loop type fingerprint was revealed in 58.29 ± 1.08% of cases; Whorl type fingerprint was revealed in 32.9±1.03% of studied individuals, while, Arched type was observed in 8.33±0.60% and only 0.48±0.15% shown Composite type of fingerprint. In our research fingerprint distribution model were as follows: Loop > Whorl > Arches > Composite. Based on obtained data on the population of Ajara, we might conclude that a variety of fingerprint patterns are not related to gender.
Distribution - Free Continuous Bayesian Belief Nets

This paper introduces distribution free continuous belief nets using the vine copulae modelling approach. Nodes are associated with arbitrary continuous invertible distributions, influences are associated with (conditional) rank correlations and are realized by (conditional) copulae. Any copula which represents (conditional) independence as zero (conditional) correlation can be used. We illustrate this approach with a flight crew alertness model.
Motion Robust Remote Photoplethysmography Measurement During Exercise for Contactless Physical Activity Intensity Detection

Monitoring the subject’s physical activity intensity (PAI) is key to preventing sports injury and enhancing conditioning performance. Existing monitoring equipment can be cumbersome and is not always accessible in daily situations due to cost or usability. For ease of usage, we propose a motion robust remote photoplethysmography (rPPG) measurement during exercise for contactless PAI detection. To account for vigorous motion during physical exercise, we leverage signal holding with motion analysis to maintain the consistency of the signal source. We then obtain robust rPPG signals via a convolutional neural network (CNN)-based rPPG constructor module composed of a dual feature extractor with signal-based targets, a base span module, and a noise elimination module. In terms of the PAI detection application, as changes in heart rate are a dominant biomarker during exercise, this is applied to estimate the training level for PAI detection. In this study, training status is classified into three levels and is estimated via a residual-learning-based architecture with sequential heart rate input. In terms of heart rate measurements, the proposed method outperforms the state-of-the-art approaches in motion scenarios on both ECG-Fitness and our own exercise datasets. The mean absolute error (MAE) is reduced to about one-third of the comparison methods in real-world treadmill running scenarios, and the error level goes down by about 30% in the ECG-Fitness dataset under a cross-dataset evaluation. In addition, for follow-up PAI detection applications, the heart rate values estimated with the proposed method approximate the heart rate ground truth. It is capable to represent the trend between training load and PAI changes. We propose a novel framework for the motion robust rPPG measurement during exercise applied to PAI detection, with which overall athlete assessment can be accomplished as a convenient alternative to existing athlete monitoring equipment.
Modelling non‐homogeneous Poisson processes with almost periodic intensity functions

Summary. We propose a model for the analysis of non‐stationary point processes with almost periodic rate of occurrence. The model deals with the arrivals of events which are unequally spaced and show a pattern of periodicity or almost periodicity, such as stock transactions and earthquakes. We model the rate of occurrence of a non‐homogeneous Poisson process as the sum of sinusoidal functions plus a baseline. Consistent estimates of frequencies, phases and amplitudes which form the sinusoidal functions are constructed mainly by the Bartlett periodogram. The estimates are shown to be asymptotically normally distributed. Computational issues are discussed and it is shown that the frequency estimates must be resolved with order o(T−1) to guarantee the asymptotic unbiasedness and consistency of the estimates of phases and amplitudes, where T is the length of the observation period. The prediction of the next occurrence is carried out and the mean‐squared prediction error is calculated by Monte Carlo integration. Simulation and real data examples are used to illustrate the theoretical results and the utility of the model.
Computational Analysis and Experimental Testing of the Molecular Mode of Action of Gatastatin and Its Derivatives

Simple Summary The glaziovianin A derivative gatastatin, presented as a γ-tubulin-specific inhibitor, could represent a viable chemotherapeutic strategy to solve the specificity issues associated with targeting α and β tubulin. Since gatastatin’s specificity for γ tubulin has not been confirmed by an in silico analysis or verified experimentally by other groups, we undertook finding a molecular-level elucidation of the binding mode of gatastatin and comparing its predicted binding affinity values for both α-β and γ tubulin. We believe that our paper opens the possibility for the rational design of a long-sought candidate drug with desired specificity and selectivity for γ tubulin. Abstract Given its critical role in cell mitosis, the tubulin γ chain represents a viable chemotherapeutic target to solve the specificity issues associated with targeting α and β tubulin. Since γ tubulin is overexpressed in glioblastoma multiforme (GBM) and some breast lesions, the glaziovianin A derivative gatastatin, presented as a γ-tubulin-specific inhibitor, could yield a successful therapeutic strategy. The present work aims to identify the binding sites and modes of gatastatin and its derivatives through molecular-docking simulations. Computational binding free energy predictions were compared to experimental microscale thermophoresis assay results. The computational simulations did not reveal a strong preference toward γ tubulin, suggesting that further derivatization may be needed to increase its specificity.
Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis

Background: Tuberculosis (TB) is one of the leading causes of death worldwide and a major cause of ill health. Without treatment, the mortality rate of TB is approximately 50%; with treatment, most patients with TB can be cured. However, anti-TB drug treatments may result in many adverse effects. Therefore, it is important to detect and predict these adverse effects early. Our study aimed to build models using an artificial intelligence/machine learning approach to predict acute hepatitis, acute respiratory failure, and mortality after TB treatment. Materials and Methods: Adult patients (age ≥ 20 years) who had a TB diagnosis and received treatment from January 2004 to December 2021 were enrolled in the present study. Thirty-six feature variables were used to develop the predictive models with AI. The data were randomly stratified into a training dataset for model building (70%) and a testing dataset for model validation (30%). These algorithms included XGBoost, random forest, MLP, light GBM, logistic regression, and SVM. Results: A total of 2248 TB patients in Chi Mei Medical Center were included in the study; 71.7% were males, and the other 28.3% were females. The mean age was 67.7 ± 16.4 years. The results showed that our models using the six AI algorithms all had a high area under the receiver operating characteristic curve (AUC) in predicting acute hepatitis, respiratory failure, and mortality, and the AUCs ranged from 0.920 to 0.766, 0.884 to 0.797, and 0.834 to 0.737, respectively. Conclusions: Our AI models were good predictors and can provide clinicians with a valuable tool to detect the adverse prognosis in TB patients early.