Miracle-dz/UniFAR-FTUs huggingface dataset:
PROJECTIVE OPERATORS IN ELEMENTARY PARTICLE THEORY

Projective matrices (diads) describing any possible state of a particle with arbitrary spin are set up with aid of minimal polynomials of the relativistic wave equation matrices and the spin matrices. The main physical quantities (energy-momentum, current-charge, transition probability) can be directly expressed in terms of these projective operators in an invariant manner (irrespective of the choice of the representation basis). As a result, calculation of various effects for any type of spin particle redances to the evaluation of the traces of certain combinations of the matrices. As applications of the method the general conditions of definiteness of energy and charge for particles of a single mass are derived and a simple deduction is presented for the general commutation relations for particle of an arbitrary spin. (tr-auth)
A new species of the genus Cottus (Scorpaeniformes, Cottidae) from the Baltic Sea Basin and its phylogenetic placement

The genus Cottus contains more than 60 species that are common in freshwater bodies of northern Eurasia and North America. Despite the abundance of species, this genus has been insufficiently studied in Eastern European rivers and lakes. The new taxon Cottus cyclophthalmus sp. nov. was found in the Neman/Nemunas and Venta river systems (Baltic Sea Basin). Cottus cyclophthalmus is sister and most morphologically similar to Cottus gobio. The new species has the following diagnostic characters: round, protruding (tubular) eyes near front of head; dermal papillae on top and sides of head, naked body, absence of bony prickles, full trunk canal with 32–36 pores. The description of Cottus cyclophthalmus and a new finding of Cottus microstomus improved understanding of the Eastern European biodiversity.
Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach.
How different data supports affect geostatistical modelling: the new aggregation method and comparison with the classical regularisation and the theoretical punctual model

ABSTRACT In geostatistical analysis, often the data have different support (volume). Data with different supports can be treated separately or together but in a consistent way. In many applications, data are considered as point variable averaged over the sample volume. Regularisation of data has a significant impact on variograms and geostatistical estimations. Two methods of managing data with different supports (“integrating”) are compared: regularisation and aggregation. This paper examines the consequences of the regularisation on sample variograms and proposes another procedure to integrate samples called “aggregation”. The variogram models of integrated data are compared with the theoretical punctual model. The point-support variogram presents some advantages, such as the possibility of utilizing samples without compositing their values. But, this implies to modify the classical kriging system. The ways of managing data with different supports are applied to a complex dataset of an iron ore body. The spatial variable analysis from the composited borehole samples demonstrates the impact of the integrating methods through cross validation. Results show the effect of the variogram model in the kriging system and the accuracy of the evaluation. Also evidenced are the sensitivity to the integration method, the selected parameters and the benefits of the theoretical punctual variogram.
“I alone can fix it”: is social courage a bright side of narcissism?

PurposeNarcissism is characterized by grandiosity, and it is included in the Dark Triad with Machiavellianism and psychopathy. Narcissism relates to many detrimental outcomes, but a growing stream of research has investigated beneficial outcomes of the Dark Triad. The authors continue this stream of research by assessing the relation of narcissism with social courage behaviors, which are prosocial actions with personal social risks. The authors propose that this relation is mediated by self-deceptive enhancement and subjective invulnerability and also test these relations while including the other Dark Triad dimensions.Design/methodology/approachThe authors perform two survey studies to investigate our research questions. The first utilizes a cross-sectional approach, whereas the second utilizes a time-separated approach.FindingsThe results of this study show that narcissism has an indirect effect on social courage via both mediators, and it also produced a significant direct effect. Machiavellianism and psychopathy did not produce any reliable effects on social courage – whether direct or indirect.Practical implicationsThe authors support that a “dark” trait relates to a “bright” outcome, but also that a bright outcome relates to a dark trait. These findings link the narcissism and social courage to novel theoretical frameworks, such as those associated with ethical decision making and self-regulation, which opens many directions for future research on both constructs.Originality/valueThe current article addresses two recent calls for novel research: the study of courage as well as positive outcomes of negative traits. It also adds much-needed nuance to current research on narcissism and courage.
An Exploration of the Effect of the Kleier Model and Carrier-Mediated Theory to Design Phloem-Mobile Pesticides Based on Researching the N-Alkylated Derivatives of Phenazine-1-Carboxylic Acid-Glycine

The Kleier model and Carrier-mediated theory are effective for molecularly designing pesticides with phloem mobility. However, the single Kleier model or Carrier-mediated theory cannot achieve a reliable explanation of the phloem mobility of all exogenous substances. A detailed investigation of the two models and the scope of their applications can provide a more accurate and highly efficient basis for the guidance of the design and development of phloem-mobile pesticides. In the present paper, a strategy using active ingredient-amino acid conjugates as mode compounds is developed based on Carrier-mediated theory. An N-alkylated amino acid is used to improve the pesticide’s physicochemical properties following the Kleier model, thus allowing the conjugates to fall on the predicted and more accessible transportation region of phloem. Moreover, the influence of this movement on phloem is inspected by the Kleier model and Carrier-mediated theory. To verify this strategy, a series of N-alkylated phenazine-1-carboxylic acid-glycine compounds (PCA-Gly) were designed and synthesized. The results related to the castor bean seeds (R. communis L.) indicated that all the target compounds (4a–4f) had phloem mobility. The capacity for phloem mobility shows that N-alkylated glycine containing small substituents can significantly improve PCA phloem mobility, such as 4c(i-C3H7-N) > 4a(CH3-N) ≈ 4b(C2H5-N) > 4d (t-C4H9-N) > PCA-Gly > 4e(C6H5-N) > 4f(CH2COOH-N), with an oil–water partition coefficient between 1.2~2.5. In particular, compounds 4a(CH3-N), 4b(C2H5-N), and 4c(i-C3H7-N) present better phloem mobility, with the average concentrations in phloem sap of 14.62 μΜ, 13.98 μΜ, and 17.63 μΜ in the first 5 h, which are 8 to 10 times higher than PCA-Gly (1.71 μΜ). The results reveal that the Kleier model and Carrier-mediated theory play a guiding role in the design of phloem-mobile pesticides. However, the single Kleier model or Carrier-mediated theory are not entirely accurate. Still, there is a synergism between Carrier-mediated theory and the Kleier model for promoting the phloem transport of exogenous compounds. Therefore, we suggest the introduction of endogenous plant compounds as a promoiety to improve the phloem mobility of pesticides through Carrier-mediated theory. It is necessary to consider the improvement of physicochemical properties according to the Kleier model, which can contribute to a scientific theory for developing phloem-mobile pesticides.
Toward Minimum WCRT Bound for DAG Tasks Under Prioritized List Scheduling Algorithms

Many modern real-time parallel applications can be modeled as a directed acyclic graph (DAG) task. Recent studies show that the worst-case response time (WCRT) bound of a DAG task can be significantly reduced when the execution order of the vertices is determined by the priority assigned to each vertex of the DAG. How to obtain the optimal vertex priority assignment, and how far from the best-known WCRT bound of a DAG task to the minimum WCRT bound are still open problems. In this article, we aim to construct the optimal vertex priority assignment and derive the minimum WCRT bound for the DAG task. We encode the priority assignment problem into an integer linear programming (ILP) formulation. To solve the ILP model efficiently, we do not involve all variables or constraints. Instead, we solve the ILP model iteratively, i.e., we initially solve the ILP model with only a few primary variables and constraints, and then at each iteration, we increment the ILP model with the variables and constraints which are more likely to derive the optimal priority assignment. Experimental work shows that our method is capable of solving the ILP model optimally without involving too many variables or constraints, e.g., for instances with 50 vertices, we find the optimal priority assignment by involving 12.67% variables on average and within several minutes on average.
Calibration-Free 3D Indoor Positioning Algorithms Based on DNN and DIFF

The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. Signal strength difference (DIFF), a calibration-free solution for handling the received signal strength variance between diverse devices, can effectively reduce the negative impact of signal fluctuation. However, DIFF also leads to the explosion of the RSSI data dimension, expanding the number of dimensions from m to Cm2, which reduces the positioning efficiency. To this end, we design a data hierarchical processing strategy based on a building-floor-specific location, which effectively improves the efficiency of high-dimensional data processing. Moreover, based on a deep neural network (DNN), we design three different positioning algorithms for multi-building, multi-floor, and specific-location respectively, extending the indoor positioning from the single plane to three dimensions. Specifically, in the stage of data preprocessing, we first create the original RSSI database. Next, we create the optimized RSSI database by identifying and deleting the unavailable data in the RSSI database. Finally, we perform DIFF processing on the optimized RSSI database to create the DIFF database. In the stage of positioning, firstly, we design an improved multi-building positioning algorithm based on a denoising autoencoder (DAE). Secondly, we design an enhanced DNN for multi-floor positioning. Finally, the newly deep denoising autoencoder (DDAE) used for specific location positioning is proposed. The experimental results show that the proposed algorithms have better positioning efficiency and accuracy compared with the traditional machine learning algorithms and the current advanced deep learning algorithms.
Evaluation of Predictive Ability of Bayesian Regularized Neural Network Using Cholesky Factorization of Genetic Relationship Matrices for Additive and Non-additive Genetic Effects

This study aimed to explore the effects of additive and non-additive genetic effects on the prediction of complex traits using Bayesian regularized artificial neural network (BRANN). The data sets were simulated for two hypothetical pedigrees with five different fractions of total genetic variance accounted by additive, additive x additive, and additive x additive x additive genetic effects. A feed forward artificial neural network (ANN) with Bayesian regularization (BR) was used to assess the performance of different nonlinear ANNs and compare their predictive ability with those from linear models under different genetic architectures of phenotypic traits. Effective number of parameters and sum of squares error (SSE) in test data sets were used to evaluate the performance of ANNs. Distribution of weights and correlation between observed and predicted values in the test data set were used to evaluate the predictive ability. There were clear and significant improvements in terms of the predictive ability of linear (equivalent Bayesian ridge regression) and nonlinear models when the proportion of additive genetic variance in total genetic variance ( ) increased. On the other hand, nonlinear models outperformed the linear models across different genetic architectures. The weights for the linear models were larger and more variable than for the nonlinear network, and presented leptokurtic distributions, indicating strong shrinkage towards 0. In conclusion, our results showed that: a) inclusion of non-additive effects did not improve the prediction ability compared to purely additive models, b) The predictive ability of BRANN architectures with nonlinear activation function were substantially larger than the linear models for the scenarios considered.
The Economics of Intangible Capital

Intangible assets are a large and growing part of firms’ capital stocks. Intangibles are accumulated via investment—foregoing consumption today for output in the future—but they lack a physical presence. Rather than stopping with this “lack,” we instead focus on the positive properties of intangibles. Specifically, intangibles must be stored, so characteristics of the storage medium have important implications for their value and use. These properties include non-rivalry, allowing the intangible to be used simultaneously in different production streams, and limited excludability, which prevents the firm from capturing all the benefits or rents from the intangible. We develop these ideas in a simple way to illustrate how outcomes such as scalability and distribution of ownership follow. We discuss how intangibles can help to understand important trends in macroeconomics and finance, including productivity, factor shares, inequality, investment and valuation, rents and market power, and firm financing.
A Critical Scrutiny on Liposomal Nanoparticles Drug Carriers as Modelled by Topotecan Encapsulation and Release in Treating Cancer

The medical field is looking for drugs and/or ways of delivering drugs without harming patients. A number of severe drug side effects are reported, such as acute kidney injury (AKI), hepatotoxicity, skin rash, and so on. Nanomedicine has come to the rescue. Liposomal nanoparticles have shown great potential in loading drugs, and delivering drugs to specific targeted sites, hence achieving a needed bioavailability and steady state concentration, which is achieved by a controlled drug release ability by the nanoparticles. The liposomal nanoparticles can be conjugated to cancer receptor tags that give the anticancer-loaded nanoparticles specificity to deliver anticancer agents only at cancerous sites, hence circumventing destruction of normal cells. Also, the particles are biocompatible. The drugs are shielded by attack from the liver and other cytochrome P450 enzymes before reaching the desired sites. The challenge, however, is that the drug release is slow by these nanoparticles on their own. Scientists then came up with several ways to enhance drug release. Magnetic fields, UV light, infrared light, and so on are amongst the enhancers used by scientists to potentiate drug release from nanoparticles. In this paper, synthesis of liposomal nanoparticle formulations (liposomal-quantum dots (L-QDs), liposomal-quantum dots loaded with topotecan (L-QD-TPT)) and their analysis (cytotoxicity, drug internalization, loading efficiency, drug release rate, and the uptake of the drug and nanoparticles by the HeLa cells) are discussed.
Transoral Robotic Surgery for Oropharyngeal Squamous Cell Carcinoma of the Tonsil versus Base of Tongue: A Systematic Review and Meta-Analysis

Simple Summary Transoral Robotic Surgery (TORS) has become widely adopted for the surgical removal of oropharyngeal squamous cell carcinoma (OPSCC). However, it is currently unknown if TORS has equal efficacy and outcomes in patients with tonsillar or base of tongue (BOT) OPSCC. Therefore, we performed a systematic review, including articles describing the surgical management of OPSCC with TORS that compared margin status, complications, and recurrence between tonsil and BOT. BOT OPSCC had a higher rate of positive margins compared to tonsillar OPSCC. However, no differences were seen in the recurrence or postoperative hemorrhage rates of BOT and tonsillar OPSCC. While a higher rate of positive margins was seen in BOT OPSCC when compared to tonsillar OPSCC, this did not translate to a higher recurrence rate in the BOT group. Future research on which subset of patients with BOT is more likely to have positive margins is warranted to improve the utility of TORS further. Abstract Transoral Robotic Surgery (TORS) has become widely adopted for the surgical removal of oropharyngeal squamous cell carcinoma (OPSCC), with the most common locations being in the tonsil and base of tongue (BOT). However, it is currently unknown if TORS has equal efficacy and outcomes in patients with tonsillar or BOT OPSCC. Therefore, the aim of this study was to compare the margin status and recurrence rates of tonsillar and BOT OPSCC after TORS. Per PRISMA guidelines, PubMed, Scopus, and CINAHL were systematically searched from inception to 2/28/2022. Articles including the surgical management of OPSCC with TORS that compared margin status, complications, and recurrence between tonsil and BOT were included. Meta-analyses of proportions and odds ratios were performed. A total of 28 studies were included, comprising 1769 patients with tonsillar OPSCC and 1139 patients with BOT OPSCC. HPV positivity was seen in 92.3% of tumors. BOT OPSCC had a higher rate of positive margins compared to tonsillar OPSCC (28.1% [95%CI 15.1–43.3] vs. 7.5% [95%CI 3.3–13.3]). No differences were seen in recurrence between BOT and tonsillar OPSCC (OR 1.1 [95%CI 0.8–1.5], p = 0.480). In addition, no differences in postoperative hemorrhage were seen between tonsillar and BOT OPSCC (10.7% [95%CI 6.1–16.5] vs. 8.8% [95% CI 1.5–21.3]). While a higher rate of positive margins was seen in BOT OPSCC when compared to tonsil OPSCC, this did not translate to a higher recurrence rate in the BOT group. Future research on which subset of patients with BOT is more likely to have positive margins is warranted to improve the utility of TORS further.
Different Intestinal Microbiota with Growth Stages of Three-Breed Hybrid Pig

The changes of intestinal microbiota are closely related to the growth and development of animals. The current study is aimed at exploring the composition of the microbial community of pigs at different growth stages. Fresh fecal samples of three-breed hybrid pigs at three developmental stages (60, 120, and 180 days of age) were collected. The microbial composition was analyzed based on the 16S rDNA gene of bacteria Illumina NovaSeq sequencing platform. The results showed that the intestinal microbiota of pigs was distributed in 22 phyla, 46 classes, 84 orders, 147 families, and 287 genera. Firmicutes, Bacteroides, Spirochaetae, Proteobacteria, and Actinobacteria were the dominant phyla. Lactobacillus, Streptococcus, SMB53, Oscillospira, and Prevotella were the dominant genera. Among them, the abundance of Lactobacillus and SMB53 increased first and then decreased, while the change of Oscillospira was opposite. In addition, the abundance of Streptococcus increased while that of Prevotella decreased gradually. Moreover, with the increase of time and body weight, the microbial diversity showed a decreasing trend. In conclusion, the intestinal microbial composition of the three-breed hybrid pigs was relatively stable during the fattening stage, but there were significant differences in abundance.
Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles

Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), this paper explores the feasibility of using GAN to improve the training datasets and obtain a better classification model, thereby improving the accuracy of classification. First, we use the generative model of GAN to generate a large datasets, which does not need manual annotation. At the same time, the discriminative model of GAN is improved to classify the types of signals based on the loss function of the discriminative model. Finally, this model can be used to the outdoor environment and obtain a real-time illegal UAVs signal classification system. Our experiments confirmed that the improvements on the Auxiliary Classifier Generative Adversarial Networks (AC-GANs) by limited datasets achieve excellent results. The recognition rate can reach more than 95% in the indoor environment, and this method is also applicable in the outdoor environment. Moreover, based on the theory of Wasserstein GANs (WGAN) and AC-GANs, a more robust Auxiliary Classifier Wasserstein GANs (AC-WGANs) model is obtained, which is suitable for multi-class UAVs. Through the combination of AC-WGANs and Universal Software Radio Peripheral (USRP) B210 software defined radio (SDR) platform, a real-time UAVs signal classification system is also implemented.