Abstract This paper seeks to empirically explore how an international financial integration influences a country’s GDP growth. The long run relationship is tested by PMG estimator for the sample of ten EU countries from Central, Eastern and Southeastern Europe (CEE-10 countries) between 1995 and 2017. Prior to the conducting of dynamic panel analysis based on PMG estimators, several panel unit root tests were conducted, as well as panel co integration tests. The findings offer mixed impact financial integration on growth. Among the measures of financial integration, growth of the CEE-10 countries is mostly driven in the long run by FDI inflows as well as remittances and financial openness. On the contrary, the study suggests a reversal relationship between growth and financial integration measured by Gross Foreign Assets and Liabilities in percentages of GDP. It might be explained with a fact that CEE-10 countries have not yet reached a certain level of financial development in order to benefit from financial integration. The study concludes that international financial integration does not per se enhance economic growth and country’s growth in the CEE-10 countries can be reached at a higher level of financial integration, further increase their financial openness and financial development.

The mesiodens is the most frequent type of supernumerary tooth which can appear in the maxillary midline area. The etiology of mesiodentes is not fully understood. This report shows a case of incomplete fusion of an unerupted mesiodens with a permanent maxillary central incisor, aligned in the dental arch. Intraoral and radiographic examinations indicated fusion of the crown and cervical part of the root of the supernumerary tooth with the permanent incisor. The clinical situation was further complicated by the presence of another supernumerary tooth located palatally. The treatment approach has included two phase surgical therapy to extract the supernumerary teeth. Early diagnosis and appropriate surgical treatment of mesiodentes are important to decrease the risk of clinical complications. Pre-operative 3D imaging is strongly advisable since it allows accurate data to be obtained, and reduces the extent of surgery and the possibility of procedural complications. In most cases, a multidisciplinary collaboration is necessary for precise diagnosis and predictable treatment outcome.

Background: The aim of this study was to identify the risk factors associated with falling in post stroke patients. Methods: This retrospective case-control study included 561 neurology patients hospitalized for a stroke and divided into two groups: falling patients and non-falling patients. They referred to the Special Hospital for Cerebrovascular Diseases “Sveti Sava” in Belgrade, Serbia, from 2018–2019. Logistic regression analysis was applied to examine socio-economic factors associated with predictors of unmet healthcare needs. Results: A significant difference was seen in the length of hospitalization of falling patients compared to the non-falling (P<0.001). We established statistically significant differences in mental status (P<0.001), sensibility (P=0.016), depressed mood (P<0.001), early (P=0.001) and medium insomnia (P=0.042), psychomotor slowness (P=0.030), somatic anxiety (P=0.044) and memory (P<0.001). Conclusion: Cerebrovascular disease distribution and the degree of neurological deficit primarily altered mental status, which could be recognized as one of the more important predictors for falling after stroke. The identification of risk factors may be a first step toward the design of intervention programs for preventing a future fall among hospitalized stroke patients.

In this paper, we propose Federated Deep Learning (FDL) for intrusion detection in heterogeneous networks. Local Deep Neural Network (DNN) models are used to learn the hierarchical representations of the private network traffic data in multiple edge nodes. A dedicated central server receives the parameters of the local DNN models from the edge nodes, and it aggregates them to produce an FDL model using the Fed+ fusion algorithm. Simulation results show that the FDL model achieved an accuracy of 99.27 ± 0.79%, a precision of 97.03 ± 4.22%, a recall of 98.06 ± 1.72%, an F1 score of 97.50 ± 2.55%, and a False Positive Rate (FPR) of 2.40 ± 2.47%. The classification performance and the generalisation ability of the FDL model are better than those of the local DNN models. The Fed+ algorithm outperformed two state-of-the-art fusion algorithms, namely federated averaging (FedAvg) and Coordinate Median (CM). Therefore, the DNN-Fed+ model is preferable for intrusion detection in heterogeneous wireless networks.

Accurate downlink channel state information (CSI) is one of the essential requirements for harnessing the potential advantages of frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The current state-of-art in this vibrant research area include the use of deep learning to compress and feedback downlink CSI at the user equipments (UEs). These approaches focus mainly on achieving CSI feedback with high reconstruction performance and low complexity, but at the expense of inflexible compression rate (CR). High training overheads and limited storage capacity requirements are some of the challenges associated with the design of dynamic CR, which instantaneously adapt to propagation environment. This paper applies transfer learning (TL) to develop a multi-rate CSI compression and recovery neural network (TL-MRNet) with reduced training overheads. Simulation results are presented to validate the superiority of the proposed TL-MRNet over traditional methods in terms of normalized mean square error and cosine similarity.

Malware traffic classification (MTC) is a key technology for solving anomaly detection and intrusion detection problems. And hence it plays an important role in the field of network security. Traditional MTC methods based on port, payload and statistic depend on the manual-designed features, which have low accuracy. Recently, deep learning methods have attracted significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this paper proposes two methods based on semi-supervised learning (SSL) and transfer learning (TL), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the accuracy classification with few labeled samples. Through experiments, we obtained the best method to improve the accuracy of few labeled samples in different situations. Experiment results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples.

Intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) multiple-input single-output (MISO) is considered one of the promising techniques in next-generation wireless communication. However, existing beamforming methods for IRS-aided mm Wave MISO systems require high computational power, so it cannot be widely used. In this paper, we combine an unsupervised learning-based fast beamforming method with IRS-aided MISO systems, to significantly reduce the computational complexity of this system. Specifically, a new beamforming design method is proposed by adopting the feature fusion means in unsupervised learning. By designing a specific loss function, the beamforming can be obtained to make the spectrum more efficient, and the complexity is lower than that of the existing algorithms. Simulation results show that the proposed beamforming method can effectively reduce the computational complexity while obtaining relatively good performance results.

Despite its tremendous potential, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Among other issues, there are hard limits to the number of qubits that can be integrated into a single chip. Multicore architectures are a firm candidate for unlocking the scalability of quantum processors. Nonetheless, the vulnerability and complexity of quantum communications make this a challenging approach. A comprehensive design should imply consolidating the communications stack in the quantum computer architecture. In this article, we explain how this vision, by entangling communications and computation in the core of the design, may help to solve the open challenges. We also summarize the first results of our application of structured design methodologies backing this vision. With our work, we hope to contribute with design guidelines that may help unleash the potential of quantum computing.

Abstract Polycyclic aromatic hydrocarbons (PAHs) are formed from anthropogenic activities, i.e. industrial emissions, incomplete combustion of petroleum, coal and other fossil fuels and other industrial and domestic activities. Research areas of this study are four representative locations in the industrial complex, in the city of Banja Luka, Republic of Srpska, Bosnia and Herzegovina. The main objective of the paper is to determine the ecological risk and to assess probable sources of PAHs contamination in soil and groundwater. The results of this study reflect the effects of coal combustion (pyrogenic origin), petrogenic and biomass origin and may provide basic data for the remediation of PAHs in the location. The ecological risk in soil (at depths of 30, 100, 200, 300 and 400 cm) and groundwater is determined. The mean values of ecological risk in soil and groundwater decreased with soil depth. Values of RQ(NCs) for groundwater were found at high ecological risk, for Ant, Chr, DahA, Acy, Pyr, BaA, Phe, Flo, Nap, Ace and Fluo, with values 28.57, 20.59, 300.00, 242.86, 185.71, 1700.0, 76.67, 53.33, 15.83, 100.00 and 57.14, respectively. ∑16PAH indicated high ecological risk for most PAHs, which decreased with soil depth. The value of RQ(NCs) for ΣPAHs in groundwater indicates high ecological risk (ΣPAHs ≥ 800 and RQ(MPCs) ≥ 1). This is the first study on the ecological risk of PAHs in soil and groundwater in industrial soils in Banja Luka and provides baseline information for further studies and additional investigations of this industrial complex.

There is not a single country in the world that is so rich that it can remove all level crossings or provide their denivelation in order to absolutely avoid the possibility of accidents at the intersections of railways and road traffic. In the Republic of Serbia alone, the largest number of accidents occur at passive crossings, which make up three-quarters of the total number of crossings. Therefore, it is necessary to constantly find solutions to the problem of priorities when choosing level crossings where it is necessary to raise the level of security, primarily by analyzing the risk and reliability at all level crossings. This paper presents a model that enables this. The calculation of the maximal risk of a level crossing is achieved under the conditions of generating the maximum entropy in the virtual operating mode. The basis of the model is a heterogeneous queuing system. Maximum entropy is based on the mandatory application of an exponential distribution. The system is Markovian and is solved by a standard analytical concept. The basic input parameters for the calculation of the maximal risk are the geometric characteristics of the level crossing and the intensities and structure of the flows of road and railway vehicles. The real risk is based on statistical records of accidents and flow intensities. The exact reliability of the level crossing is calculated from the ratio of real and maximal risk, which enables their further comparison in order to raise the level of safety, and that is the basic idea of this paper.