Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Modality of Engagement: Conceptualization, Typology of Forms, and Outcomes

Customers proactively engage with firms’ offerings through behavioral manifestations such as brand-related social media posts, influencing other customers in online networks and, consequently, affecting brand value. With the growth of visually oriented social media platforms, interest has increased in understanding customer engagement behavior (CEB) using visual content. In this paper, we build on CEB, image acts, visual content, and communication theories to conceptualize the Visual Modality of Engagement (VME). Using both field and lab studies, we develop a typology of four distinct positive (experiential, evidential) and negative (mocking, dissuasive) forms of VME and offer empirical evidence revealing they induce different brand-related (purchase intentions, brand evaluation) and other customer-related (willingness to imitate, resharing intentions) outcomes. Additional results also reveal outcomes vary by the interplay of social and brand interactions with the various VME forms. The findings of this research offer guidance to content managers for the development of more effective engagement strategies in social media marketing.
Time in Physics and the Human Mind: A System-Theoretical Approach

The authors elaborate on the subject of time perception raised by R. Gruber, R. Block and C. Montemayor. Time is probably the most enigmatic and multifaceted notion, both in physics and psychology. It does not have one, clear or concise definition. Formulating such a definition seems to be hardly probable, for each definition may include only a few selected aspects of a definiendum. In this paper, the authors present physical and psychological perspectives of time. The former includes the mathematically describable concepts of time proposed by I. Newton and A. Einstein. The latter introduces a systemic outlook on motor operation construction in humans. Its ‘backbone’ is the modalities’ ladder, derived from N. Bernstein’s theory and the fact that movement is the only manifestation of all human mental activity. Each of the particular modalities of information processing – proprioceptive, contactceptive, teleceptive, verbal and symbolic – includes its specific, respective perception of time. The authors also discuss the concept of the IGUS system created by J. Hartle, the aim of which is bridging the gap between physics and psychology. However, in the presented paper an attempt is made to invent a general view on the structure of time perception in psychology and anthropokinetics, on the basis of System-Theoretical Approach (STA).
A New Image Quality Assessment Algorithm based on SSIM and Multiple Regressions

Image quality assessment (IQA) is crucial in image processing algorithms. In the state-of-the-art IQA index, the structural similarity (SSIM) index has been proved to be better objective quality assessment metric. However, the accuracy of SSIM is relatively lacking when used to access blurred images. And the component weights of structural similarity (SSIM) index are fixed in some past environments. So an improved assessment algorithm incorporating multiple linear regressions and SSIM index was proposed in this paper. We use regression analysis to adjust the component weight of SSIM index. So the improved algorithm is more accuracy on different distortion types’ quality assessment. Experimental results show that the improved SSIM algorithm is better than traditional methods in nonlinear regression correlation coefficient, Spearman correlation coefficient and out ratio.
A class of equivalent problems in statistics and operational research

A broad class of equivalent problems in Statistics and Operational Research, including problems relating to nonparametric maximum likelihood estimation for finite mixture models, to indirect estimation involving Poisson processes, toi? ½ the construction of Bayesian D-optimal designs for one-parameter nonlineari? ½ models, and to the Kalai-Nash solution to simple bargaining games, is discussed. In particular some key results pertaining to the generic problem are drawn together from the finite mixture distribution and the optimal design literature and the advantages which accrue from recognizing this class of equivalent problems are emphasized. The import of the findings is highlighted by means of examples involving maximum likelihood image reconstruction for PET scans and the selection of optimal investment portfolios.
IMPACT OF ORGANIZATIONAL CULTURE ON EMPLOYEE PERFORMANCE

Purpose: If you look at the big picture a society’s culture determines how people behave, respond, and interact with one another and how fast the society can grow as a community. This definition holds true for organizational culture too. The purpose of this research is to find out the impact of organizational culture on employee performance, how engaged employees are at work depends on how happy and comfortable they feel about the organizational culture. Methodology: The method used in this article is a qualitative method, where we describe how organizational culture affects employee performance. The data collection technique is done by observation through articles available on trusted journal pages. Findings: The goal of an organization is to increase level of performance by designing strategies. Based on the results of data analysis, organizational culture helps retain top performance and valuable talent. It creates a culture of engagement and increases employee productivity It makes people happy and excited about the workplace. How culture affects employee engagement about 87% of organizations agree that employee engagement and culture are the two most vital workplace challenges.
Variability in Low Mass X-ray binaries from milliseconds to years

Sixteen years of observations of black-hole transients with the Rossi X-ray Timing Explorer, complemented by other X-ray observatories and ground-based optical/infrared/radio telescopes have given us a clear view onto the complex phenomenology associated to their bright outbursts. This has led to the definition of a small number of spectra/timing states which are separated by marked transition in observables. The association of these states and their transitions to changes in the radio emission from relativistic radio jets complete the picture and have led to the study of the connection between accretion and ejection. A good number of fundamental questions are still unanswered, but the existing picture provides a good framework on which to base theoretical studies. We discuss the current observational standpoint, with emphasis onto the spectral and timing evolution during outbursts, as well as the prospects for future missions such as ASTROSAT (2012) and LOFT (>2020 if selected).
Attenuation of Seismic Random Noise With Unknown Distribution: A Gaussianization Framework

Random noise attenuation is a critical step in seismic data processing. Since the distribution of field noise is complex and unknown, this poses a challenge to noise attenuation methods where the default noise distribution is known, such as a Gaussian distribution. To address this issue, we propose a method called seismic random noise Gaussianization framework (SRNGF) to attenuate random noise with unknown distribution. Specifically, SRNGF couples the Gaussianization subproblem and Gaussian denoising subproblem based on the plug-and-play (PaP) framework. The Gaussianization submodule with learnable parameters maps seismic data with the noise of unknown distribution to the one corrupted by Gaussian noise. The learnable parameters can be updated through unsupervised online training according to the noise of the unknown distribution on a single field data. The Gaussian denoising submodule, which can be replaced by seismic denoiser, aims only for Gaussian noise removal, making SRNGF incorporate the denoiser prior to seismic data. Thus, we incorporate different kinds of seismic [nondeep/deep learning (DL)] denoisers into SRNGF and give their corresponding implementations of SRNGF. Experiments on noise with unknown distribution qualitatively and quantitatively validate the superiority of SRNGF. SRNGF improves the results of non-DL/DL seismic denoiser by a large margin.
A Novel Local Binary Temporal Convolutional Neural Network for Bearing Fault Diagnosis

In bearing fault diagnosis, the faulty data are generally limited due to the high cost of fault signal collection. Considering the excessive parameters in the traditional convolutional neural network (CNN), such a limited data issue can cause overfitting problem during the model training, eventually resulting in poor fault diagnosis performance. To resolve the overfitting issue and elevate the diagnostic accuracy of the conventional methods, a novel fault diagnosis method based on local binary temporal CNN (LBTCNN) is proposed in this article. In the proposed LBTCNN, a novel temporal module with dilated causal convolution for deep feature extraction is proposed to increase model depth under limited model parameters, and a local binary convolution (LBC) layer is adopted to reduce the computational parameters. To evaluate the effectiveness of the proposed method, several experiments under different scenarios such as limited samples and different noise levels are conducted on two datasets, including the rolling bearing accelerated life test dataset of Xi’an Jiaotong University and Changxing Sumyoung Technology (XJTU-SY), and the motor bearing dataset of Case Western Reserve University (CWRU). The comparison results demonstrate that the LBTCNN method is superior over six other prominent fault diagnosis approaches under different bearing operation stages, different training samples, and different signal-to-noise ratios (SNRs).
Safety Evaluation of the Polyherbal Formulation NawaTab: Acute and Subacute Oral Toxicity Studies in Rats

NawaTab is a tablet formulation developed from the Nawametho polyherbal formula used in Surat Thani province, Southern Thailand, for the treatment of hyperlipidemia. This study aims at evaluating the acute and subacute toxicity of NawaTab in rats. In the acute toxicity study, NawaTab was evaluated in female rats following the OECD Guideline No. 423. In the subacute toxicity study, NawaTab was tested in both male and female rats following the OECD Guideline No. 407. In the acute toxicity study, no lethal effects or toxic signs were observed during the duration of the study. In the subacute toxicity study, there was no mortality and no abnormality in clinical signs, body weight, food consumption, relative organ weight, and hematological parameters of NawaTab-treated rats. Significantly increased water consumption by male rats (500 mg/kg BW) and female rats (250, 500, and 1000 mg/kg BW) was observed. In addition, globulin and total cholesterol of female rats (1000 mg/kg BW) significantly increased. These alterations were within normal physiological ranges. Moreover, necropsy and histopathological findings of NawaTab-treated rats demonstrated no obvious alterations attributable to NawaTab administration. The present study revealed that NawaTab has no significant acute oral toxicological effects. The lethal dose with a 50% mortality rate (LD50) was higher than 5000 mg/kg BW in rats. The subacute oral administration of NawaTab for 28 days did not have any major toxicological effects. Based on this study, NawaTab could be safe to use with caution pending its chronic toxicity study.
Microlensing model fitting with MulensModel

We introduce MulensModel, a software package for gravitational microlensing calculations. The package provides a framework for calculating microlensing model magnification curves and goodness-of-fit statistics for microlensing events with single and binary lenses as well as a variety of higher-order effects: extended sources with limb-darkening, annual microlensing parallax, and satellite microlensing parallax. The software could also be used for analysis of the planned microlensing survey by the NASA flag-ship WFIRST satellite. MulensModel is available at this https URL
Type-2 Hesitant Fuzzy Sets

ABSTRACT By using type-2 fuzzy sets and hesitant fuzzy sets, type-2 hesitant fuzzy sets are defined and their mathematical structure and characteristics are given. The relations of the structures of type-2 hesitant fuzzy sets and hesitant fuzzy sets are further studied. Consequently, we prove that type-2 hesitant fuzzy sets are the generalization of hesitant fuzzy sets. Type-2 hesitant fuzzy sets may deal with the problem that hesitant fuzzy sets can't have repeated memberships. Otherwise, a part of the special type-2 hesitant fuzzy sets can be changed into discrete type-2 fuzzy sets, but their operations have many differences. On dealing with the fact problems, type-2 hesitant fuzzy sets are the better methods to solve the problems and can be easy to get better results.
Hybrid TH precoding with subarray connection for multi‐user mmWave MIMO systems

Existing linear algorithms based on subarray connection (SC) structures may cause noise enhancement and performance degradation. To solve this problem, this paper presents a nonlinear hybrid Tomlinson‐Harashima precoding (THP) algorithm based on SC structure, named as THP‐SC algorithm. In the proposed algorithm, the sum‐rate optimization problem can be transformed into two equivalent optimization subproblems including the transmitter optimization subproblem and the receiver optimization subproblem. These two subproblems are easy to solve. Furthermore, the analog precoding and combining matrices of the optimization subproblem are solved by a new improved successive interference cancelation (SIC) algorithm based on the singular value threshold shrinkage (SVS), which can effectively reduce the computational complexity while eliminating the interference. In the end, the digital precoding and combining matrices are obtained by TH method. Through the joint optimization of the transmitter and receiver matrices in the system, the optimal solution is obtained by alternate iteration of the matrices. In this paper, a more realistic line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) channel system is also considered. Simulations verify that the proposed algorithm can improve the spectral efficiency and energy efficiency to some extent with a comparable complexity compared to the previous works.
Segmentation et détection d'objets par caractérisation multi-échelle

Half away between edge detection and object recognition this study aim to characterize singularities in order to guide this two phases. The caracterization is obtained with a generalization of the Lipschitz exponents . for complex structures. This new characterization is called " maxima chain " and integrates the notion of value and spatial repartition of the maxima. The maxima chains are obtained with a multiscale decomposition using a Diadic Discrete Wavelet Transform (DDWT). We use the maxima of the gradient image computed with the wavelet details. The local maxima detect the position of irregular structures (edges) in the image (Canny algorithm). Then we characterize each maxima using their evolution across the different scales. A classification is made using the maxima chains. We integrate the classification to eliminate unwanted maxima (from noise or other objects) to drive the search of closed contours. In fact, there is a totally different signature from one maxima to another depending on the parameters of the objects : nature, shape, size, gray scale, texture, .
Diabetic peripheral neuropathy - advances in diagnosis and treatment

Diabetes mellitus is one of the most alarming conditions of the 21st century. The prevalence of both type 1 and 2 diabetes is constantly increasing, which also entails a growing number of patients suffering from its various complications. One of the most common chronic complications is diabetic neuropathy (DN), which is developed by at least 50% of patients with diabetes. Diabetic peripheral neuropathy (DPN) is the most frequent type of DN. Clinically, a non-painful and a painful-DPN can be distinguished. The painful type causes a massive decrease in the quality of life and affects around 30% of individuals with DM. In recent years many instruments have been used to diagnose and treat DPN but none of them seems to be sufficient in early diagnosis and satisfactory symptoms management. To date, there is any gold standard to recognize DNP and usually it is noticed when a patient presents symptoms of neuropathy, which is too late to introduce an effective therapy. The main approaches for patients with DNP focus on improving glycaemic control, lifestyle modifications and pain therapy. Multiple medicaments are used for this purpose, among which SNRIs, TCAs and anticonvulsants are most effective. Non-pharmacologic treatment, such as acupuncture, physical therapy, nerve stimulation, also has been used, however their effectiveness remains uncertain.
Epidemiology of Antimicrobial Resistance Genes in Staphyloccocus aureus Isolates from a Public Database in a One Health Perspective—Sample Characteristics and Isolates’ Sources

Staphylococcus aureus is considered one of the most widespread bacterial pathogens for both animals and humans, being the causative agent of various diseases like food poisoning, respiratory tract infections, nosocomial bacteremia, and surgical site and cardiovascular infections in humans, as well as clinical and subclinical mastitis, dermatitis, and suppurative infections in animals. Thanks to their genetic flexibility, several virulent and drug-resistant strains have evolved mainly due to horizontal gene transfer and insurgence of point mutations. Infections caused by the colonization of such strains are particularly problematic due to frequently occurring antibiotic resistance, particulary methicillin-resistant S. aureus (MRSA), and are characterized by increased mortality, morbidity, and hospitalization rates compared to those caused by methicillin-sensitive S. aureus (MSSA). S. aureus infections in humans and animals are a prime example of a disease that may be managed by a One Health strategy. In fact, S. aureus is a significant target for control efforts due to its zoonotic potential, the frequency of its illnesses in both humans and animals, and the threat posed by S. aureus antibiotic resistance globally. The results of an epidemiological analysis on a worldwide public database (NCBI Pathogen Detection Isolate Browser; NPDIB) of 35,026 S. aureus isolates were described. We considered the diffusion of antibiotic resistance genes (ARGs), in both human and animal setting, and the results may be considered alarming. The result of this study allowed us to identify the presence of clusters with specific ARG patterns, and that these clusters are associated with different sources of isolation (e.g., human, non-human).
Integrative Metabolome and Transcriptome Analyses Reveal the Molecular Mechanism of Yellow-Red Bicolor Formation in Kalanchoe blossfeldiana Petals

The winter pot kalanchoe (Kalanchoe blossfeldiana) is an ornamental plant with succulent leaves and clustered flowers in Crassulaceae, widely used as a potted flower or garden decoration. In nature, the bicolor petal is an interesting phenomenon, and breeders have succeeded in cultivating the winter pot kalanchoe with bicolored petals. However, its potential molecular mechanism of pigmentation is poorly understood. This study collected a yellow-red colored winter pot kalanchoe to investigate the molecular mechanism underlying its bicolor formation using the integrative analyses of metabolome and transcriptome. The metabolome results showed that both flavonoid and carotenoid co-existed in the winter pot kalanchoe petals, whereas only anthocyanin accumulation showed significant differences—about nineteen times higher in the red region than that in the yellow region. The differentially expressed genes were significantly enriched in the anthocyanin biosynthesis pathway, and the expression level of biosynthetic genes, including KbCHS, KbCHI, KbF3H, KbDFR, KbANS and KbGTs, were significantly upregulated in the red region. Moreover, transcription factors potentially regulating anthocyanin biosynthesis were predicted, and KbMYB2 and KbbHLH1 might play important roles in positively regulating anthocyanin biosynthesis in the red region. The findings reported here provide new insights into the understanding of petal bicolor formation mechanisms and will assist cultivar innovation in winter pot kalanchoe.
Enhanced in-plane mechanical properties of nanoporous graphene-carbon nanotube network

Three dimensional graphene-carbon nanotube networks (3D-GC) have attracted great interests due to their superior thermal, optical, and hydrogen storage properties. In our work, the in-plane mechanical properties of nanoporous 3D-GC with different diameters of the joint carbon nanotube (CNT) and porosity have been studied. During in-plane tension, the fracture of 3D-GC first initiates at the heptagonal defects of the junctions between graphene sheets and CNTs where large tensile residual stress is observed. The in-plane tensile strength of 3D-GC decreases with the increasing of CNT parameter and porosity, and the tensile modulus is mainly determined by the porosity. Although the fracture strain decreases with the CNT diameter, it increases with the porosity. Compared to the nanoporous graphene, 3D-GC has larger in-plane tensile strength and fracture strain due to the additional support of CNTs. However, the in-plane tensile modulus of 3D-GC is usually smaller than that of the nanoporous graphene due to the.
Stochastic modelling and diffusion modes for POD models and small-scale flow analysis

We introduce a stochastic modelling in the constitution of fluid flow reduced-order models. This framework introduces a spatially inhomogeneous random field to represent the unresolved small-scale velocity component. Such a decomposition of the velocity in terms of a smooth large-scale velocity component and a rough, highly oscillating, component gives rise, without any supplementary assumption, to a large-scale flow dynamics that includes a modified advection term together with an inhomogeneous diffusion term. Both of those terms, related respectively to turbophoresis and mixing effects, depend on the variance of the unre-solved small-scale velocity component. They bring to the reduced system an explicit subgrid term enabling to take into account the action of the truncated modes. Besides, a decomposition of the variance tensor in terms of diffusion modes provides a meaningful statistical representation of the stationary or nonstationary structuration of the small-scale velocity and of its action on the resolved modes. This supplies a useful tool for turbulent fluid flows data analysis. We apply this methodology to circular cylinder wake flow at Reynolds numbers Re = 300 and Re = 3900, respectively. The finite dimensional models of the wake flows reveal the energy and the anisotropy distributions of the small-scale diffusion modes. These distributions identify critical regions where corrective advection effects as well as structured energy dissipation effects take place. In providing rigorously derived subgrid terms, the proposed approach yields accurate and robust temporal reconstruction of the low-dimensional models.