Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Sliding Mode Control for Flexible Dual-manipulator with Friction Compensation

This paper focuses on the coordinated tracking control scheme of dual-manipulator based on friction compensation. First, a new dual-manipulator model with flexible joints and friction is constructed; Second, a new adaptive neural control method is proposed for the dual-manipulators with flexible joints. Neural networks are used to approximate the unknown nonlinear dynamics in the model; Third, new state observers are constructed to estimate the joint friction. Based on the observers, the friction can be well compensated. The stability of the system is derived by using Lyapunov method. The simulation results verify the effectiveness of the practical method.
FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards

Modern AI supported research holds many promises for basic and applied science. However, the application of AI methods is often limited because most labs cannot, on their own, acquire large and diverse datasets, which are best for training these methods. Data sharing and open science initiatives promise some relief to the problem, but only if the data are provided in a usable way. The FAIR principles state very general requirements for useful data sharing: they should be findable, accessible, interoperable, and reusable. This article will focus on two challenges to implement the FAIR framework for human neuroscience data. On the one hand, human data can fall under special legal protection. The legal frameworks regulating how and what data can be openly shared differ greatly across countries which can complicate data sharing or even discourage researchers from doing so. Moreover, openly accessible data require standardization of data and metadata organization and annotation in order to become interpretable and useful. This article briefly introduces open neuroscience initiatives that support the implementation of the FAIR principles. It then reviews legal frameworks, their consequences for accessibility of human neuroscientific data and some ethical implications. We hope this comparison of legal jurisdictions helps to elucidate that some alleged obstacles for data sharing only require an adaptation of procedures but help to protect the privacy of our most generous donors to research … our study participants. Finally, it elaborates on the problem of missing standards for metadata annotation and introduces initiatives that aim at developing tools to make neuroscientific data acquisition and analysis pipelines FAIR by design. While the paper focuses on making human neuroscience data useful for data-intensive AI the general considerations hold for other fields where large amounts of openly available human data would be helpful.
Changes in magnetic parameters of neutron irradiated SA 508 Cl. 3 reactor pressure vessel forging and weld surveillance specimens

Irradiation-induced changes in the magnetic parameters and mechanical properties were measured and compared to explore possible correlations for reactor pressure vessel (RPV) forging and weld surveillance Charpy specimens which were irradiated to the neutron fluence of 2.3×1019 n/cm2 (E>1.0 MeV) in a typical pressurized water reactor environment at 290 °C. For mechanical property parameters, Vickers microhardness, tensile and Charpy impact tests were performed and saturation magnetization (Ms), remanence (Mr), coercivity (Hc), and Barkhausen noise amplitude (BNA) were measured for magnetic parameters for both unirradiated and irradiated specimens, respectively. Results of mechanical property measurements showed an increase in yield and tensile strength, Vickers microhardness, 30 ft. lb indexed RTNDT and a decrease in Charpy upper-shelf energy irrespective of forging and weld metals. Hysteresis loops appeared to turn clockwise, resulting in an increase in Hc, and BNA appeared to decrease after irradiation.
Phytochemical-Based Nanomaterials against Antibiotic-Resistant Bacteria: An Updated Review

Antibiotic-resistant bacteria (ARB) is a growing global health threat, leading to the search for alternative strategies to combat bacterial infections. Phytochemicals, which are naturally occurring compounds found in plants, have shown potential as antimicrobial agents; however, therapy with these agents has certain limitations. The use of nanotechnology combined with antibacterial phytochemicals could help achieve greater antibacterial capacity against ARB by providing improved mechanical, physicochemical, biopharmaceutical, bioavailability, morphological or release properties. This review aims to provide an updated overview of the current state of research on the use of phytochemical-based nanomaterials for the treatment against ARB, with a special focus on polymeric nanofibers and nanoparticles. The review discusses the various types of phytochemicals that have been incorporated into different nanomaterials, the methods used to synthesize these materials, and the results of studies evaluating their antimicrobial activity. The challenges and limitations of using phytochemical-based nanomaterials, as well as future directions for research in this field, are also considered here. Overall, this review highlights the potential of phytochemical-based nanomaterials as a promising strategy for the treatment against ARB, but also stresses the need for further studies to fully understand their mechanisms of action and optimize their use in clinical settings.
Convergence analysis of operator splitting methods for the Burgers-Huxley equation

CONVERGENCE ANALYSIS OF OPERATOR SPLITTING METHODS FOR THE BURGERS-HUXLEY EQUATION The purpose of this thesis is to investigate the implementation of the two operator splitting methods; Lie-Trotter splitting and Strang splitting method applied to the BurgersHuxley equation and prove their convergence rates in Hs(R), for s ≥ 1. The analyses are based on the properties of the Sobolev spaces. The Burgers-Huxley equation is deal with the two parts; linear and non-linear parts. The regularity results are shown by using the same technique in (Holden, Lubich and Risebro, 2013) for both parts. By combining these results with the numerical quadratures and the Peano Kernel theorem error bounds are derived for the first and second order splitting methods. In the computational part, the operator splitting methods are applied to the Burgers-Huxley equation. Finally, the convergence rates for the two splitting methods are checked numerically. These numerical results confirmed the theoretical results.
Cyclodextrin Polymers as a Promising Drug Carriers for Stabilization of Meropenem Solutions

Here we report the development of new drug carriers for meropenem based on the hydroxypropyl-β-cyclodextrin (HPCD) polymers with variable linkers, namely, 1,6-hexamethylenediisocyanate (HMD), citric acid (CA), succinic anhydride (SA). The structures of obtained polyesters and polyurethanes nanoparticles (120–200 nm) were investigated by NMR and FTIR-spectroscopy. The PXRD pattern demonstrated that HPCD polymers form complexes with meropenem (MP), and the majority of MP molecules are encapsulated into a complex. MP’s imprinting in the HPCD-HMD polymer matrix lead to an encapsulation efficiency of up to 82%. HPCD-HMD and HPCD-SA polymers increase MP’s stability during the storage of its aqueous solution (in 1.4 and 1.2 times, respectively). In contrast, HPCD-CA polymer negatively affects MP’s stability. In prospect, the HPCD-HMD polymer may be promising for the development of a highly efficient drug delivery system for MP.
Contributions to the Theory of Monte Carlo and Quasi-Monte Carlo Methods

of the Dissertation CONTRIBUTIONS TO THE THEORY OF MONTE CARLO AND QUASIMONTE CARLO METHODS by Giray Ökten The Claremont Graduate School: 1997 Quasi-Monte Carlo methods, which are often described as deterministic versions of Monte Carlo methods, were introduced in the 1950s by number theoreticians. They improve several deficiencies of Monte Carlo methods; such as providing estimates with deterministic bounds and avoiding the paradoxical difficulty of generating random numbers in a computer. However, they have their own drawbacks. First, although they provide faster convergence than Monte Carlo methods asymptotically, the advantage may not be practical to obtain in "high" dimensional problems. Second, there is not a practical way to measure the error of a quasi-Monte Carlo simulation. Finally, unlike Monte Carlo methods, there is a scarcity of error reduction techniques for these methods. In this dissertation, we attempt to provide remedies for the disadvantages of quasi-Monte Carlo methods mentioned above. In the first part of the dissertation, a hybrid-Monte Carlo sequence designed to obtain error reduction in high dimensions is studied. Probabilistic results on the discrepancy of this sequence as well as results obtained by applying the sequence to problems from numerical integration and mathematical finance are presented. In the second part of the dissertation, a new hybrid-Monte Carlo method is introduced, in an attempt to obtain a practical statistical error analysis using low-discrepancy sequences. It is applied to problems from mathematical finance and particle transport theory to compare its effectiveness with the conventional methods. In the last part of the dissertation, a generalized quasi-Monte Carlo integration rule is introduced. A Koksma-Hlawka type inequality for the rule is proved, using a new concept for the variation of a function. As a consequence of the rule, error reduction techniques and in particular an "importance sampling" type statement are derived. Problems from different disciplines are used as practical tests for our methods. The numerical results obtained in favor of the methods suggest the practical advantages that can be realized by their use in a wide variety of applications.
Determining the Control Objectives of a Switched Reluctance Machine for Performance Improvement in Generating Mode

This article presents a methodology to determine the control objectives of conduction angle control in generating mode of operation in a switched reluctance machine. First, the performance in motoring mode of control is compared with generating mode for different operating points. Then, the key optimization objectives are established to improve a switched reluctance machine's performance in generating mode. A multiobjective optimizer is used to select the conduction angles. The proposed generating-specific objectives are maximizing source current per torque and minimizing torque ripple. These objectives are then compared with the motoring-specific objectives, such as maximizing average torque and minimizing torque ripple for a wide speed range. Finally, the proposed generating objectives have been validated experimentally using a three-phase 12/8 switched reluctance machine.
Calculating the Limits of Detection in Laser-Induced Breakdown Spectroscopy: Not as Easy as It Might Seem

The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and its evolution towards the new official definition recently adopted by the International Union of Pure and Applied Chemistry (IUPAC) will be illustrated. Methods for extending the calculation of the LOD to LIBS multivariate analysis will also be discussed, using as an example the detection of Cu traces in cast iron samples by LIBS.
U-Net multi-modality glioma MRIs segmentation combined with attention

Glioma, the most common primary intracranial tumor, is known as the “brain killer,” accounting for 27% of all central nervous system tumors and 80% of malignant tumors, and is one of the most difficult and refractory tumors to treat in neurosurgery. The development of medical imaging technology has simplified the diagnosis of the disease, and in order to avoid or reduce the errors of manual segmentation, deep learning based segmentation of glioma has become the hope of radiologists and clinicians. Accurate segmentation of gliomas is an important prerequisite for making glioma diagnosis, providing treatment plans and evaluating treatment outcomes. To effectively target the characteristics of multimodal glioma MRI and the shortcomings of CNNs-based, U-Net-based glioma segmentation methods, a method of 2D-CNNs segmentation results based on attention mechanism is proposed. In this study, the datasets of BraTS2018 and BraTS2019 were included and the segmentation results were evaluated using three metrics: Dice coefficient, positive predictive value, and sensitivity. The experimental results show that the proposed segmentation method can accurately segment gliomas.
Evaluation on the Efficacy and Safety of Panax Notoginseng Saponins in the Treatment of Stroke among Elderly People: A Systematic Review and Meta-Analysis of 206 Randomized Controlled Trials

Background Evidence regarding the effect of Panax notoginseng saponins (PNS) on treating elderly stroke patients is scare and inconsistent. This study investigated the efficacy and safety of PNS by means of meta-analysis so as to provide an evidence-based reference for the treatment of elderly patients with stroke. Methods We searched the PubMed, Embase, Cochrane Library, Web of Science, CNKI, VIP, Wanfang, and China Biomedical Database to identify the eligible randomized controlled trials (RCTs) concerning using PNS to treat elderly people with stroke from their inception to first, May 2022. Meta-analysis was used for pool analysis of the included studies, whose quality was assessed via Cochrane Collaboration's RCT risk of bias tool. Results Altogether 206 studies published between 1999 and 2022 with a low risk of bias were included, covering 21,759 participants. The results showed that the improved neurological status shown in the intervention group with PNS alone was statistically significant (SMD = −0.826, 95% CI: −0.946 to −0.707) in contrast to the control group. The total clinical efficacy (Relative risk (RR) = 1.197, 95% Confidence interval (CI): 1.165 to 1.229) and daily living activities (SMD = 1.675, 95% C: 1.218 to 2.133) of elderly stroke patients were significantly improved as well. In addition, the invention group using PNS combined with WM/TAU displayed significant improvement in neurological status (SMD = −1.142, 95% CI: −1.295 to −0.990) and the total clinical efficacy (RR = 1.191, 95% CI: 1.165 to 1.217) compared with the control group. Conclusion Single PNS intervention or PNS combined with WM/TAU significantly improves the neurological status, the overall clinical efficacy and daily living activities of elderly stroke patients. However, more multicenter RCT research with high quality is required in the future to verify the results in this study. The trial registration number: Inplasy protocol 202330042. doi:10.37766/inplasy2023.3. 0042.
Weld Seam Segmentation in RGB-D Data using Attention-based Hierarchical Feature Fusion

Accurate weld detection, segmentation, and positioning based on 3D vision is an important prerequisite for guiding the robots to perform weld milling. It is difficult to accurately segment only by 2D images because multiple welds with different morphological characteristics on the same workpiece may be located in multiple different planes, the lighting conditions sometimes change, and overexposure or underexposure may occur. In response, the method for weld instance segmentation based on the hierarchical feature fusion of RGB-D has been proposed, which can extend the module of RGB-D hierarchical feature fusion based on Mask R-CNN feature pyramid network (FPN). The fusion module introduces channel attention and spatial attention to realize the cross-modality fusion of RGB features and depth features in space. In specific, MaskGIoU Head has been added to the Mask R-CNN model to improve the accuracy of weld seam positioning, and the feature pyramid network (FPN) has been built in Mask Head to improve the boundary quality of the weld segmentation. The method presented in this paper has been proved effective and accurate by the results of the experiment.
Predictive importance of the visceral adiposity index and atherogenic index of plasma of all-cause and cardiovascular disease mortality in middle-aged and elderly Lithuanian population

Background Two indices: visceral adiposity index (VAI) and atherogenic index of plasma (AIP) during several recent years were implemented into epidemiological studies for predicting of cardiovascular diseases (CVD) and mortality risk. Our study aimed to evaluate the association of VAI and AIP with the risk of all-cause and CVD mortality among the Lithuanian urban population aged 45–72 years. Methods In the baseline survey (2006–2008), 7,115 men and women 45–72 years of age were examined within the framework of the international study Health, Alcohol and Psychosocial Factors in Eastern Europe (HAPIEE). Six thousand six hundred and seventy-one participants (3,663 women and 3,008 men) were available for statistical analysis (after excluding 429 respondents with the missed information on study variables) and for them, VAI and AIP were calculated. The questionnaire evaluated lifestyle behaviors, including smoking and physical activity. All participants in the baseline survey were followed up for all-cause and CVD mortality events until December 31st, 2020. Multivariable Cox regression models were applied for statistical data analysis. Results After accounting for several potential confounders, higher levels of VAI (compared 5th quintile to 1st quintile) were associated with significantly higher CVD mortality in men [Hazards ratio (HR) = 1.38] and all-cause mortality in women (HR = 1.54) after 10-year follow-up. CVD mortality significantly increased in men with 0 the highest AIP quintile compared with that for the lowest quintile (HR = 1.40). In women, all-cause mortality was significantly higher for the 4th quintile of AIP as compared with the 1st quintile (HR = 1.36). Conclusions High-risk VAI levels were statistically significantly associated with all-cause mortality risk in men and women groups. The higher AIP level (5th quintile vs. 1st quintile—in men and 4th quintile vs. 1st quintile—in women) was significantly associated with increased mortality from CVD in the men group and increased all-cause mortality in the women group.
Efficient Somatic Embryogenesis, Regeneration and Acclimatization of Panax ginseng Meyer: True-to-Type Conformity of Plantlets as Confirmed by ISSR Analysis

Panax ginseng Meyer grows in east Russia and Asia. There is a high demand for this crop due to its medicinal properties. However, its low reproductive efficiency has been a hindrance to the crop’s widespread use. This study aims to establish an efficient regeneration and acclimatization system for the crop. The type of basal media and strength were evaluated for their effects on somatic embryogenesis, germination, and regeneration. The highest rate of somatic embryogenesis was achieved for the basal media MS, N6, and GD, with the optimal nitrogen content (≥35 mM) and NH4+/NO3− ratio (1:2 or 1:4). The full-strength MS medium was the best one for somatic embryo induction. However, the diluted MS medium had a more positive effect on embryo maturation. Additionally, the basal media affected shooting, rooting, and plantlet formation. The germination medium containing 1/2 MS facilitated good shoot development; however, the medium with 1/2 SH yielded outstanding root development. In vitro-grown roots were successfully transferred to soil, and they exhibited a high survival rate (86.3%). Finally, the ISSR marker analysis demonstrated that the regenerated plants were not different from the control. The obtained results provide valuable information for a more efficient micropropagation of various P. ginseng cultivars.
Entropy-Based Technical Analysis Indicators Selection for International Stock Markets Fluctuations Prediction Using Support Vector Machines

Most of works on stock price forecasting are concerned with the problem of predicting its future value. However, forecasting stock price future fluctuation trend could be easier and interesting for traders and investors to maximize profits. The purpose of this study is to predict CAC40, FTSE, NASDAQ and S&P500 price index up and down fluctuations. In particular, it aims to propose a methodology to forecast regime switches in these markets time series to assist traders and investors in decision making. In the first stage, a large set composed of twenty five technical analysis indicators is formed. They fall into four broad categories namely oscillators, stochastic measures, indexes and indicators. Entropy statistic is employed to rank the initial technical analysis indicators. Finally, in the third stage, polynomial-based kernel support vector machines (SVM) are used for predicting CAC40, FTSE, NASDAQ and S&P500 future upward and downward fluctuations. The forecasting results show that the choice of technical analysis indicators used to predict CAC40 and NASDAQ fluctuations depend on the type of risk-aversion and risk-appetite of the investor. For the S&P500 and FTSE, technical analysis indicators used in our study can detect future downshifts with high accuracy. Thus, they are suitable for market analysis and trading by risk-averse investors on these markets.
Complete Chloroplast Genome of Four Thai Native Dioscorea Species: Structural, Comparative and Phylogenetic Analyses

The chloroplast genomes of Dioscorea brevipetiolata, D. depauperata, D. glabra, and D. pyrifolia are 153,370–153,503 bp in size. A total of 113 genes were predicted, including 79 protein-coding sequences (CDS), 30 tRNA, and four rRNA genes. The overall GC content for all four species was 37%. Only mono-, di-, and trinucleotides were present in the genome. Genes adjacent to the junction borders were similar in all species analyzed. Eight distinct indel variations were detected in the chloroplast genome alignment of 24 Dioscorea species. At a cut-off point of Pi = 0.03, a sliding window analysis based on 25 chloroplast genome sequences of Dioscorea species revealed three highly variable regions, which included three CDS (trnC, ycf1, and rpl32), as well as an intergenic spacer region, ndhF-rpl32. A phylogenetic tree based on the complete chloroplast genome sequence displayed an almost fully resolved relationship in Dioscorea. However, D. brevipetiolata, D. depauperata, and D. glabra were clustered together with D. alata, while D. pyrifolia was closely related to D. aspersa. As Dioscorea is a diverse genus, genome data generated in this study may contribute to a better understanding of the genetic identity of these species, which would be useful for future taxonomic work of Dioscorea.
Overcoming bandwidth limitation of light‐emitting diode in visible light communication using differential pulse amplitude modulation

Abstract. A modulation format of differential pulse amplitude modulation in visible light communication using a single transmitter module composed of multiple semiconductor light‐emitting diode (LED) chips is proposed and experimentally demonstrated. Precoded different nonreturn‐zero on–off‐keying signals were assigned to each LED chip, and linearly overlapped signals were detected by a single photodiode. In this demonstration, pre‐equalization and optical filtering were applied to compensate the modulation bandwidth limitation and nonlinearity of the light source. Data rate up to 200 Mbit/s with less than 7.8×10−5 bit error rate was verified by experiment using the proposed modulation method.