This study investigates the indirect mechanisms relevant to converting young adults' prosocial attitudes and individual responsibility into their prosocial behavior. Our results are based on a sample of 530 young adults studying at three public regional business schools in South East Europe. They show a significant favorable influence on young adults' civic and political involvement, mediating the relationship between individual responsibility attitudes and prosocial behavior. However, this would not have been expected based on previous research. Another indirect path between the same variables is modeled using a hypothesized moderated mediation effect. The institutional influence of higher education proves to be a significant mediator of the proposed relationship, moderated by the amount of educational content in the fields of ethics, social and environmental responsibility. At mid-and-high levels of exposure to relevant educational content, this indirect path significantly influences the developing young adults' pro-environmental behaviors. The study results are discussed from the viewpoint of peripheral regions with a history of dysfunctional social capital mechanisms.

Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%.

Endurance-type disciplines (running, cycling, biathlon) define the cyclic structure of an athlete's movements, which, in addition to functional parameters, also includes an adequate fitness profile. Based on the detection, analysis and evaluation of these parameters, it is possible to define the fitness profile of the competitors as well as possible mutual differences. The current case study analyzes the fitness profile of competitors of three different disciplines (middle and long distances, cycling, ski biathlon) of top-level competitors, members of national teams. The study was conducted: Uroš Gutić (UG) - runner middle and long distances, member of AK "Sarajevo" and the BIH athletic national team; Milan Milivojević (MM) – cyclist, member of Cycling club "Borac" Čačak (Serbia), and the member Serbian national team; Stefan Lopatić (SL) – ski biathlete, member SK "Romanija" Pale, and BIH national team. A total of 12 parameters were measured to assess the fitness profile (repetitive strength, explosive strength, speed and agility). The results recorded considerable homogeneity of the sample with mutual differences. In addition to mutual differences, all study participants are characterized by an extremely good fitness profile, as shown by the measurement results.  Article visualizations:

Antibody-drug conjugates (ADCs) are novel, highly potent drugs composed of a small molecule of an anticancer drug (payload)attached to humanized antibody recognizing an epitope on the surface of cancer cells. ADCs are rapidly expanding in the oncology field. By 2022, >180 ADC-based clinical trials have been conducted [1]. Most of these clinical trials are in phases I or II [1]. Several ADCs have been approved and used for the treatment of various malignancies (e.g., brentuximab vedotin (BV) for the treatment of CD30+ lymphomas, trastuzumab emtansine (T-DM1) for advanced/metastatic/or early-stage high-risk HER2-positive breast cancer with residual disease after neoadjuvant treatment) [2]. Read more in the PDF.

Deaf children, due to the impossibility of transforming the internal speech scheme into expressive speech in mutual written communication, make maximum use of abbreviated speech schemes that determine the ability to communicate. The aim of the study is to examine the content comprehensibility and semantic level of written communication of deaf children through the number of used types oaf words in written communication, and to determine the existence of statistical significaance of differences between deaf and hearing respondents in the use of word types at the level of statistical significance p=0.001. The study was conducted on a sample of 140 respondents. The first subsample of respondents, the experimental group consisted of 70 deaf students, and the second subsample, a control group of 70 hearing students, of the same chronological age and gender. The measurement instrument “Test of understanding the written form of expression” was applied. The frequencies and percentages of responses to each of the variables used were calculated. The t-test and the F (Fisher) test were used to determine the statistical significance of the differences between deaf and hearing subjects. The results of the study showed that deaf students do not have contextual understanding and recognition of word types through testing of linguistic competence in relation to the hearing population, because 67.10% do not understand, and 10.00% of deaf students partially understand the contextual application of word types in writing textual task. Deaf students in the written form of communication use nouns (65.70%), pronouns (34.30%), verbs (45.7%), adjectives (28.60%), adverbs (22.90), prepositions (54.30%), exclamations (15.70%), particles (12.90%) and numbers (32.90%). There is a statistically significant difference between hearing and deaf respondents, in favor of hearing, in all applied types of words, except the use of verbs.

Background Gastrointestinal (GI) functions are controlled by the enteric nervous system (ENS) in vertebrates, but data on snakes are scarce, as most studies were done in mammals. However, the feeding of many snakes, including Crotalus atrox , is in strong contrast with mammals, as it consumes an immense, intact prey that is forwarded, stored, and processed by the GI tract. We performed immunohistochemistry in different regions of the GI tract to assess the neuronal density and to quantify cholinergic, nitrergic, and VIPergic enteric neurons. We recorded motility patterns and determined the role of different neurotransmitters in the control of motility. Neuroimaging experiments complemented motility findings. Results A well-developed ganglionated myenteric plexus (MP) was found in the oesophagus, stomach, and small and large intestines. In the submucous plexus (SMP) most neurons were scattered individually without forming ganglia. The lowest number of neurons was present in the SMP of the proximal colon, while the highest was in the MP of the oesophagus. The total number of neurons in the ENS was estimated to be approx. 1.5 million. In all regions of the SMP except for the oesophagus more nitric oxide synthase+ than choline-acetyltransferase (ChAT)+ neurons were counted, while in the MP ChAT+ neurons dominated. In the SMP most nerve cells were VIP+, contrary to the MP, where numerous VIP+ nerve fibers but hardly any VIP+ neuronal cell bodies were seen. Regular contractions were observed in muscle strips from the distal stomach, but not from the proximal stomach or the colon. We identified acetylcholine as the main excitatory and nitric oxide as the main inhibitory neurotransmitter. Furthermore, 5-HT and dopamine stimulated, while VIP and the ß-receptor-agonist isoproterenol inhibited motility. ATP had only a minor inhibitory effect. Nerve-evoked contractile responses were sodium-dependent, insensitive to tetrodotoxin (TTX), but sensitive to lidocaine, supported by neuroimaging experiments. Conclusions The structure of the ENS, and patterns of gastric and colonic contractile activity of Crotalus atrox are strikingly different from mammalian models. However, the main excitatory and inhibitory pathways appear to be conserved. Future studies have to explore how the observed differences are an adaptation to the particular feeding strategy of the snake.

In this paper we are interested to model quantum signal by statistical signal processing methods. The Gaussian distribution has been considered for the input quantum signal as Gaussian state have been proven to a type of important robust state and most of the important experiments of quantum information are done with Gaussian light. Along with that a joint noise model has been invoked, and followed by a received signal model has been formulated by using convolution of transmitted signal and joint quantum noise to realized theoretical achievable capacity of the single quantum link. In joint quantum noise model we consider the quantum Poisson noise with classical Gaussian noise. We compare the capacity of the quantum channel with respect to SNR to detect its overall tendency. In this paper we use the channel equation in terms of random variable to investigate the quantum signals and noise model statistically. These methods are proposed to develop Quantum statistical signal processing and the idea comes from the statistical signal processing.

In this article, we are proposing a closed-form solution for the capacity of the single quantum channel. The Gaussian distributed input has been considered for the analytical calculation of the capacity. In our previous couple of papers, we invoked models for joint quantum noise and the corresponding received signals; in this current research, we proved that these models are Gaussian mixtures distributions. In this paper, we showed how to deal with both of cases, namely (I)the Gaussian mixtures distribution for scalar variables and (II) the Gaussian mixtures distribution for random vectors. Our target is to calculate the entropy of the joint noise and the entropy of the received signal in order to calculate the capacity expression of the quantum channel. The main challenge is to work with the function type of the Gaussian mixture distribution. The entropy of the Gaussian mixture distributions cannot be calculated in the closed-form solution due to the logarithm of a sum of exponential functions. As a solution, we proposed a lower bound and a upper bound for each of the entropies of joint noise and the received signal, and finally upper inequality and lower inequality lead to the upper bound for the mutual information and hence the maximum achievable data rate as the capacity. In this paper reader will able to visualize an closed-form capacity experssion which make this paper distinct from our previous works. These capacity experssion and coresses ponding bounds are calculated for both the cases: the Gaussian mixtures distribution for scalar variables and the Gaussian mixtures distribution for random vectors as well.

In this paper we present an averaging technique applicable to the design of zeroth-order Nash equilibrium seeking algorithms. First, we propose a multi-timescale discrete-time averaging theorem that requires only that the equilibrium is semi-globally practically stabilized by the averaged system, while also allowing the averaged system to depend on ``fast"states. Furthermore, sequential application of the theorem is possible, which enables its use for multi-layer algorithm design. Second, we apply the aforementioned averaging theorem to prove semi-global practical convergence of the zeroth-order information variant of the discrete-time projected pseudogradient descent algorithm, in the context of strongly monotone, constrained Nash equilibrium problems. Third, we use the averaging theory to prove the semi-global practical convergence of the asynchronous pseudogradient descent algorithm to solve strongly monotone unconstrained Nash equilibrium problems. Lastly, we apply the proposed asynchronous algorithm to the connectivity control problem in multi-agent systems.

The ammonium nitrate (AN) and fuel oil (FO) mixture known as ANFO is a typical representative of non-ideal explosives. In contrast to ideal explosives, the detonation behavior of ANFO exhibits a strong dependence on charge diameter, existence, and properties of confinement, with a large failure diameter and long distance required to establish steady-state detonation. In this study shock initiation and propagation of detonation in ANFO were studied experimentally by determining the detonation velocity at different distances from the initiation point, as well as by numerical modeling using AUTODYN hydrodynamics code and a Wood–Kirkwood detonation model incorporated into EXPLO5 thermochemical code. The run-to-steady-state detonation velocity distance was determined as a function of charge diameter, booster charge mass, and confinement. It was demonstrated that a Lee–Tarver ignition and growth reactive flow model with properly calibrated rate constants was capable of correctly ascertaining experimentally observed shock initiation behavior and propagation of detonation in ANFO, as well as the effects of charge diameter, booster mass, and confinement.

We provide a new efficient version of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e.g., convolutional or linear). We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in end-to-end runtime experiments, both in transfer learning using already-sparsified networks, and in training sparse networks from scratch. Thus, our results provide the first support for sparse training on commodity hardware.

The effectiveness of the e-tax system in encouraging tax compliance has been largely unexplored. Thus, the current study aims to examine the interrelationship between technological predictors in explaining tax compliance intention among certified tax professionals. Based on the literature on information system success and tax compliance intention, this paper proposed an expanded conceptual framework that incorporates convenience and perception of reduced compliance costs as predictors and satisfaction as a mediator. The data were collected from 650 tax professionals who used e-Filing and 492 who used e-Form through an online survey and analyzed using hierarchical multiple regression. The empirical results suggest that participants’ perceived service quality of e-Filing services and perceptions of reduced compliance costs positively influence users’ willingness to comply with tax regulations. The latter predictor is also, and only, significant among e-Form users. The empirical results also provide statistical evidence for the mediating role of satisfaction in the relationship between all predictors and tax compliance intention. This study encourages tax policymakers and e-tax filing providers to improve their services to increase user satisfaction and tax compliance.