Miracle-dz/UniFAR-FTUs huggingface dataset:
A new shrinkage function and wavelet shrinkage denoising

The WaveShrink tecdhnique is one of the major methods of denoising in the wavelet domain,which is based on the principle of shrinking wavelet coefficients towards zero to remove noise.By choosing proper threshold and appling a non-linear shrinkage rule to the wavelet coefficients,the noise in signals can be well restrained,and choosing appropriate shrinkage function in WaveShrink is one of the most basic issues,which will have great effect on denoising.In this paper,the feature and shortcoming of hard,soft and semisoft shrinkage function are discussed,and a new shrinkage function is presented,which is continuous in the whole wavelet domain and has higher-order derivatives.The new shrinkage function only needs to confirm one threshold,which makes the algorithm simpler relatively.Simulation results indicate that the effect of denoising by adopting the new shrinkage function is better.
Comparison of Efficacy and Safety of Levetiracetam Versus Phenytoin for Post-craniotomy Seizure Prophylaxis

Background: Superiority of levetiracetam over phenytoin for postcraniotomy seizure prophylaxis in patients with a supratentorial brain tumor is controversial. We aimed to evaluate the efficacy of levetiracetam versus phenytoin for postcraniotomy seizure prophylaxis in supratentorial brain tumor. Methods: In a randomized controlled trial study, 80 patients with a supratentorial brain tumor who underwent craniotomy were allocated to levetiracetam or phenytoin group, 40 patients each. Seizure prophylaxis was started 5 days before the surgery and continued until 90 days after surgery. Phenytoin group received 100 mg oral phenytoin 3 times a day. The levetiracetam group received 500 mg oral levetiracetam 2 times a day. The primary outcome was the incidence of postcraniotomy seizures. The secondary outcome measure was the safety profile of the drugs. Results: All patients of the phenytoin group and 39 patients of levetiracetam completed the study. Two seizures developed in the study population, 1 in the phenytoin group (2.5%) and 1 in the levetiracetam group (2.6%) (P = 0.710). Renal or hepatic dysfunction was not observed in any patients. Wound hematoma was seen in 5 patients (12.5%) of the phenytoin and 6 patients (15.4%) of the levetiracetam group (P = 0.481). Skin rash developed in 3 patients (7.5%) of the phenytoin group and no patient of the levetiracetam group (P = 0.132). Thrombocytopenia was detected in 1 patient of the phenytoin group (2.5%) and no patient of the levetiracetam group (P = 0.511). None of the adverse events led to drug withdrawal. Conclusion: These results reveal no superiority of levetiracetam over phenytoin for postcraniotomy seizure prophylaxis in supratentorial brain tumor.
Parameterization of the Two-Point Velocity Correlation Function in a Two-Particle Lagrangian Stochastic Model and Its Effect on the Prediction of Concentration Variances due to a Line Source

A Lagrangian stochastic model, in which a new parameterization of the two-point velocity correlation function is included, is investigated. Model simulations of temperature variances caused by a line source in an inhomogeneous wind-tunnel boundary layer are shown to be in good agreement with experimental data. A special limit of the new parameterization that is useful at large distances from the source is discussed in detail. A new technique is introduced that greatly enhances the computational efficiency of an implementation of this limit of the model and that renders long-distance simulations of concentration variances of tracers dispersing from line sources computationally treatable.
Comparison of the Persistence of Anti-TNF Agents and Ustekinumab in Patients with Crohn’s Disease: A Study Based on the Korean National Database

Background: Biologics play an important role in the treatment of moderate to severe Crohn’s disease (CD). Ustekinumab was approved for such patients in the Republic of Korea on 1 December 2018. Therefore, we need to compare the efficacy of ustekinumab and anti-TNF inhibitors. Methods: We compared one-year persistence rates between anti-TNF inhibitors and ustekinumab in moderate-to-severe CD patients using Korean National Health Insurance Service data from 1 December 2016 to 30 November 2021. We also analysed the risk factors for the non-persistence of biologics. Results: The one-year persistence rates with index therapy in bio-naïve and bio-experienced patients were 87.7% and 69.7% for infliximab (p < 0.001), 85.1% and 72.8% for adalimumab (p < 0.001), and 92.1% and 89.8% for ustekinumab (p = 0.333), respectively. The risk factors for non-persistence were older age, non-use of an immune modulator, and previous biologic exposure in both the infliximab and adalimumab groups. The one-year persistence rate of ustekinumab was higher than that of anti-TNF inhibitors in bio-naïve patients (hazard ratio [HR] 0.53; 95% confidence interval [CI] 0.35–0.81; p = 0.003) and bio-experienced patients (HR 0.32; 95% CI 0.22–0.45; p < 0.001). Conclusions: Ustekinumab was superior in bio-naïve CD patients compared to anti-TNF inhibitors. However, the follow-up time was relatively short; further studies should continuously collect and analyse data.
The O'Nan-Scott Theorem for Finite Primitive Permutation Groups, and Finite Representability

Introduction 11 Preliminaries 31.1 Centralizers and Normalizers . 31.2 Group Actions . 51.3 Sylow Subgroups . 91.4 Subdirect Products . 121.5 Minimal Normal Subgroups . 161.6 Wreath Products . 191.7 Solvable Groups . 241.8 Nilpotent Groups . 261.9 Fixed-point-free Automorphisms . 291.10 Finite Simple Groups . 332 Finite Primitive Permutation Groups 372.1 Primitivity . 372.2 Aﬃne Type . 472.3 Twisted Wreath Type . 482.4 Almost Simple Type . 502.5 Diagonal Type . 512.6 Product Type . 552.7 The O’Nan-Scott Theorem . 573 Finitely Representing
Arrival modelling for molecular communication via diffusion

The arrival of molecules in molecular communication via diffusion obeys, by its nature, the binomial distribution, considering the hitting probability as the success probability. It is, however, hard to work with the binomial cumulative distribution function (CDF) when considering consecutively sent symbols as it is necessary to add the distribution. Therefore, in the literature, two approximations of the binomial distribution are used. In the present reported work, the Gaussian and Poisson approximations of the binomial model of the molecule arrival process have been analysed. Considering the distance and the number of emitted molecules, the regions in which the Poisson or Gaussian model is better in terms of root mean squared error of CDFs are investigated and the regions using numerical simulations are confirmed.
Coating of Carbon Nanotube with Nickel by Electroless Plating Method

A method to fabricate one dimensional nanoscale composite based on coating of carbon nanotube with metal is proposed and demonstrated in this letter. Carbon nanotubes have been coated with a layer of nickel by electroless plating. It was found that the nickel coating had a tendency to form as nanoparticles on the surface of carbon nanotube and on the net of carbon nanotubes. High density active sites and low deposition rate were found to be critical for getting better coating.
Relationships have value, the impact of Leader Member Exchange on Affective Commitment

This research contributes into further investigating the impact of Leader Member Exchange (LMX) upon work attitude (i.e. affective commitment). A Study of (N=177) employees from banking sector was conducted and it was found that the LMX has significant positive association with affective commitment (AC). These significant results have shown the importance of quality of Leader Member Exchange and its impact in attaining the positive organizational outcomes.
Jellyfish Peptide as an Alternative Source of Antioxidant

Jellyfish is a valuable biological resource in marine ecosystems, and blooms been observed in numerous coastal regions. However, their utility is limited by their high water content. Recent research has focused on extracting antioxidants from marine sources. In this study, we obtained jellyfish peptides (JPHT-2) through enzymatic hydrolysis of lyophilized jellyfish powder under optimal conditions and measured their antioxidant activity. Our findings indicate that JPHT-2 possesses significant radical-scavenging activity and reducing power. At a concentration of 0.74 mg/mL, JPHT-2 exhibited a remarkable ability to scavenge hydroxyl radicals, with a rate of up to 50%. The EC50 values for scavenging superoxide anion and DPPH radical were 1.55 mg/mL and 1.99 mg/mL, respectively. At the cellular level, JPHT-2 was able to protect HaCaT cells from H2O2-induced oxidative damage by increasing the level of superoxide dismutase (SOD) in cells. In conclusion, jellyfish peptides with low molecular weight can be easily obtained through hydrolysis with three enzymes and exhibit excellent antioxidant activity and safety. Jellyfish can serve as a promising source of antioxidants.
The Influence of Q-Angle and Gender on the Stair-Climbing Kinetics and Kinematics of the Knee

Background: Knee joint motion and quadriceps activity play a crucial role in all lower limb tasks, especially those which are highly dynamic and weight-bearing. Due to anatomical differences between men and women such as height, leg length, and hip width, alignment and mechanics of the lower limb are different between males and females. An anatomical variable which is associated with alignment in the lower limb is the quadriceps muscle angle (q-angle). The purpose of this study is to determine if there is a relationship between q-angle, activity of the quadriceps and hamstring muscles and the kinetics and kinematics of the knee during stairclimbing. An investigation on the reliability of q-angle measurements was also made prior to the primary study. Methods: To test the interclass reliability of q-angle measurements, three individuals measured the q-angle on 20 subjects. The primary researcher measured the same twenty individuals on three separate days to determine intra-rater reliability. The primary study involved 10 male and 10 female subjects completing 20 stair-climbing trials (10 ascent, 10 descent). Kinematic and kinetic data were collected on the lower limbs as well as electromyography (EMG) on two quadriceps muscles and one hamstring muscle. Knee joint peak and occurrence of peak moments, average EMG amplitude and peak and occurrence of peak EMG were analyzed by gender and high and low q-angle. A two way analysis of variance (ANOVA) was used to test the statistical significance of each measured variable (α = 0.05).
Diversity of the genus Cryobacterium and proposal of 19 novel species isolated from glaciers

The bacterial genus Cryobacterium includes at present 14 species that live in cryospheric environments. In this study, we analyzed 101 genomes of Cryobacterium with pure cultures obtained from GenBank. They could be classified into 44 species based on average nucleotide identity (ANI) analysis, showing the diversity of Cryobacterium. Among these, 19 strains in our laboratory were isolated from the glacier samples in China. The pairwise ANI values of these 19 strains and known species were <95%, indicating that they represented 19 novel species. The comparative genomic analysis showed significant differences in gene content between the two groups with a maximum growth temperature (Tmax) of ≤ 20°C and a Tmax of >20°C. A comprehensive and robust phylogenetic tree, including 14 known species and 19 novel species, was constructed and showed five phylogenetic branches based on 265 concatenated single-copy gene sequences. The Tmax parameter had a strong phylogenetic signal, indicating that the temperature adaptation of Cryobacterium was largely through vertical transfer rather than horizontal gene transfer and was affected by selection. Furthermore, using polyphasic taxonomy combined with phylogenomic analysis, we proposed 19 novel species of the genus Cryobacterium by the following 19 names: Cryobacterium serini sp. nov., Cryobacterium lactosi sp. nov., Cryobacterium gelidum sp. nov., Cryobacterium suzukii sp. nov., Cryobacterium fucosi sp. nov., Cryobacterium frigoriphilum sp. nov., Cryobacterium cryoconiti sp. nov., Cryobacterium lyxosi sp. nov., Cryobacterium sinapicolor sp. nov., Cryobacterium sandaracinum sp. nov., Cryobacterium cheniae sp. nov., Cryobacterium shii sp. nov., Cryobacterium glucosi sp. nov., Cryobacterium algoritolerans sp. nov., Cryobacterium mannosilyticum sp. nov., Cryobacterium adonitolivorans sp. nov., Cryobacterium algoricola sp. nov., Cryobacterium tagatosivorans sp. nov., and Cryobacterium glaciale sp. nov. Overall, the taxonomy and genomic analysis can improve our knowledge of phenotypic diversity, genetic diversity, and evolutionary characteristics of Cryobacterium.
Learning Low-dimensional SAR Target Representations from Few Samples

Due to the lack of raw data, difficulty in labeling, and limitations of sensor characteristics, Few-Shot Learning (FSL) in Synthetic Aperture Radar (SAR) images remains a significant challenge. Inspired by the geometric distribution of data, a Disentangled Feature-based Adversarial Latent AutoEncoder (DF-ALAE) model is proposed for representing typical targets in SAR images, which models the intrinsic attributes of targets and perturbation of scenes, respectively. It fuses the prior distribution information with the limited training samples to reduce sample complexity for model optimization. Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to evaluate the representation and generalization ability of the DF-ALAE. When the number of training samples is 121, it achieves a classification accuracy of 93.10%. Comparative experimental results demonstrate the effectiveness and superiority of the proposed model.
Constructing Microstructural Evolution System for Cement Hydration From Observed Data Using Deep Learning

Cement has been widely used in civil engineering directly and plays a critical role in cement-based materials, e.g., concrete. As the microstructural evolution of cement hydration predominates the final physical properties, an accurate simulation of hydration is highly required to enable scientists to evaluate the performance and help design new cementitious materials. However, despite significant effort and progress, a satisfactory model to realistically and accurately simulate the evolution of three-dimensional (3-D) microstructure has not yet to be constructed, mainly because cement hydration is one of the most complex phenomena in material science. In this work, a novel near-realistic microstructural model is proposed to simulate the cement hydration system using deep learning and cellular automata. It is designed to break through the bottleneck of fidelity to real microstructural evolution. The dynamical system is constructed based on a 3-D cellular automaton, in which behavior is controlled by deep neural networks distilled from microstructural images. In addition, a dynamic stratified sampling method with variable capacity is proposed to ensure the representativeness of samples for reducing the computation cost of training. Experiments manifest that the simulated hydration is in accordance with the actual development in different aspects, such as near-realistic microstructure and approximate process. Furthermore, the constructed system also demonstrates promising generalization capability even under various conditions.
Indeterminacy in Foreign Exchange Markets

We discuss price variations distributions in foreign exchange markets, characterizing them both in calendar and business time frameworks. The price dynamics is found to be the result of two distinct processes, a multi-variance diffusion and an error process. The presence of the latter, which dominates at short time scales, leads to indeterminacy principle in finance. Furthermore, dynamics does not allow for a scheme based on independent probability distributions, since volatility exhibits a strong correlation even at the shortest time scales.
A Reliability Evaluation and Inspection-Based Replacement Optimization Policy Considering Measurement Variability and State variations

Inspection and inspection-based maintenance is an important measure in engineering asset management, due to its capacity to alleviate health deterioration. During inspection, many plants confront with inspection errors due to measurement accuracy and other unexpected noises, which is further complicated by random state transitions. To address the problem, this study investigates the reliability modeling and evaluation approach for a two-phase continuously degrading system subject to measurement variability. Accordingly, a state-driven, multi-threshold replacement policy is devised. Upon an inspection, the hidden defect and the real-time degradation are revealed, followed by replacement decision made according to the predetermined control limit considering the measurement variability. By jointly optimizing the inspection interval and replacement control limit, the operational cost is minimized. A numerical experiment is given to verify the profitability of the proposed policy over cost reduction and availability improvement.
Design of a Wideband Circularly Polarized Orthomode Transducer for 5G Applications

A wideband septum-based orthomode transducer (OMT) is designed to characterize and measure dual circularly polarized 5G communication systems. The OMT is designed for the n260 (37–40 GHz) and n259 (39.5-43.5 GHz) bands. The fabricated OMT has 20% fractional bandwidth (36–44 GHz) with a measured return loss and isolation better than 18 dB. The OMT achieves a simulated cross polarization discrimination (XPD) and axial ratio (AR) of 42.5 dB and 0.45 dB respectively. Good agreement between simulations and measurements is obtained.
Satisfaction Maximization Between Data and Computing Capacities in UAV-Based MEC Network

In this paper, we investigate the data node multiple access schemes in unmanned aerial vehicle (UAV)-based network. The data node-UAV assignment process is formulated as a coalition formation game (CFG). A Max-satisfaction (Max-sat) order is proposed for maximizing the satisfaction between the collocated data and the UAVs computing capacities. Besides, the CFG with Max-sat order is theoretically proved as an exact potential game, which has at least one Nash equilibrium (NE) point. The CFG-based data node-UAV assignment algorithm is proposed to achieve the NE and maximize satisfaction. Simulation results have shown that our proposed scheme has better performance than Selfish order, Pareto order and the nearest scheme.
ProcGuard: Process Injection Behaviours Detection Using Fine-grained Analysis of API Call Chain with Deep Learning

New malware increasingly adopts novel fileless techniques to evade detection from antivirus programs. Process injection is one of the most popular fileless attack techniques. This technique makes malware more stealthy by writing malicious code into memory space and reusing the name and port of the host process. It is difficult for traditional security software to detect and intercept process injections due to the stealthiness of its behavior. We propose a novel framework called ProcGuard for detecting process injection behaviors. This framework collects sensitive function call information of typical process injection. Then we perform a fine-grained analysis of process injection behavior based on the function call chain characteristics of the program, and we also use the improved RCNN network to enhance API analysis on the tampered memory segments. We combine API analysis with deep learning to determine whether a process injection attack has been executed. We collect a large number of malicious samples with process injection behavior and construct a dataset for evaluating the effectiveness of ProcGuard. The experimental results demonstrate that it achieves an accuracy of 81.58% with a lower false-positive rate compared to other systems. In addition, we also evaluate the detection time and runtime performance loss metrics of ProcGuard, both of which are improved compared to previous detection tools.
A Proactive and Generalizable Conflict Prediction Model

This study presents several technical innovations on the usage of machine learning towards analyzing, monitoring, and predicting collaboration success from multiparty dialogue for the development of a virtual agent to support effective teamwork. First, we examine the problem of predicting team performance from embeddings learned from multiparty dialogues such that teams with similar conflict scores lie close to one another in vector space. We extract the embeddings from three types of features: 1) dialogue acts, 2) sentiment polarity 3) syntactic entrainment. Although all of these features can be used to predict team performance effectively, their utility varies by the teamwork phase. We separate the dialogues of players playing a cooperative game into stages: 1) early (knowledge building), 2) middle (problem-solving), and 3) late (culmination). Unlike syntactic entrainment, both dialogue act and sentiment embeddings effectively classify team performance, even during the initial phase.Second, we address the problem of learning generalizable models of collaboration. Machine learning models often suffer domain shifts; one advantage of encoding the semantic features is their adaptability across multiple domains. We evaluate the generalizability of different embeddings to other goal-oriented teamwork dialogues. Finally, in addition to identifying features predictive of successful collaboration, we aim to improve the generalizability of collaborative task success prediction models under natural distribution shifts and resource scarcity using a combination of domain invariant and domain specific features. Research is conducted on several multiparty dialogue datasets, including Teams, ASIST, and GitHub.
A CAD System for Lung Cancer Detection Using Hybrid Deep Learning Techniques

Lung cancer starts and spreads in the tissues of the lungs, more specifically, in the tissue that forms air passages. This cancer is reported as the leading cause of cancer deaths worldwide. In addition to being the most fatal, it is the most common type of cancer. Nearly 47,000 patients are diagnosed with it annually worldwide. This article proposes a fully automated and practical system to identify and classify lung cancer. This system aims to detect cancer in its early stage to save lives if possible or reduce the death rates. It involves a deep convolutional neural network (DCNN) technique, VGG-19, and another deep learning technique, long short-term memory networks (LSTMs). Both tools detect and classify lung cancers after being customized and integrated. Furthermore, image segmentation techniques are applied. This system is a type of computer-aided diagnosis (CAD). After several experiments on MATLAB were conducted, the results show that this system achieves more than 98.8% accuracy when using both tools together. Various schemes were developed to evaluate the considered disease. Three lung cancer datasets, downloaded from the Kaggle website and the LUNA16 grad challenge, were used to train the algorithm, test it, and prove its correctness. Lastly, a comparative evaluation between the proposed approach and some works from the literature is presented. This evaluation focuses on the four performance metrics: accuracy, recall, precision, and F-score. This system achieved an average of 99.42% accuracy and 99.76, 99.88, and 99.82% for recall, precision, and F-score, respectively, when VGG-19 was combined with LSTMs. In addition, the results of the comparison evaluation show that the proposed algorithm outperforms other methods and produces exquisite findings. This study concludes that this model can be deployed to aid and support physicians in diagnosing lung cancer correctly and accurately. This research reveals that the presented method has functionality, competence, and value among other implemented models.