Introduction The optimal management of distal ureteral stones remains a matter of debate since current guidelines favor ureteroscopy over extracorporeal shock wave lithotripsy (ESWL). We aimed to evaluate the efficiency of ESWL for distal ureteral stones and to identify factors that affect treatment outcomes. Materials and methods The retrospective study included records of 115 patients with distal ureteral stones, 5 mm to 18 mm in size, undergoing 223 ESWL sessions as an outpatient procedure. Early fragmentation and three-month follow-up stone-free rate (SFR) was assessed through radiographic imaging. Treatment was successful if there were no residual fragments or they were ≤4 mm, three months after the last session. Results The mean ±standard deviation (range) stone size was 9.68 ±3.10 (5.00-18.0) mm. The mean body mass index (BMI) was 24.3 ±2.67 (18.4-29.8) kg/m² with a significant correlation between BMI and stone size (r2 =0.324, p <0.001). Patients underwent ESWL an average of 1.7 ±1.36 times (1-5), while 68 patients (59.1%) became stone-free after one session. The overall SFR was 82.6%; for patients with stone sizes ≤10 mm and >10 mm, it was 99% and 9.4%, respectively. Cumulative SFR after the second session was 77%. In 20 (17%) patients the treatment was a failure. Complications occurred in 10.4%, while auxiliary procedures were needed in 8.7% of cases, both significantly affected by the stone size (p <0.001). The efficiency quotient (EQ) was 0.76. Treatment outcome was significantly different depending on stone size, BMI, number of sessions, complications, and auxiliary procedures (p <0.001, p =0.022, p <0.001, p <0.001, p <0.001, respectively). Univariate regression analysis identified stone size and BMI as significant predictors of treatment outcome (odds ratio (OR) 3.84, 95% confidence interval (CI): 2.31-8.97, p =0.001, and OR 1.25, 95% CI: 1.04-1.54, p =0.024, respectively). Conclusions Extracorporeal shock wave lithotripsy continues to be a safe and effective option for managing simple calculi in distal ureters with a diameter of ≤10 mm. The stone size and BMI remain significant predictors of treatment outcome.

Objective. The aim of this study was to carry out the cultural adaptation and validation of the Assessment of Chronic Illness Care questionnaire (ACIC) in the Republika Srpska, Bosnia and Herzegovina. Methods. A validation study was conducted in two randomly selected primary health care centers in the Republika Srpska, Bosnia and Herzegovina, during March and April 2016. The study participants were all physicians working in family medicine departments during the study. Translation of the ACIC questionnaire version 3.5 was performed following the guidelines of the World Health Organization. The validity and reliability of the questionnaire were tested with face validity, construct validity, and internal consistency. Results. The questionnaire was distributed to 66 family physicians. Missing values were negligible, therefore the criteria for factor analysis were met. Exploratory factor analysis confirmed that the questionnaire measured one factor. The Cronbach alpha coefficient (0.970) showed the excellent level of internal consistency of the questionnaire. The intraclass correlation coefficient (0.802) confirmed the good reliability of the questionnaire. Conclusion. The ACIC questionnaire can be used to assess the quality of chronic care in family medicine practice in Bosnia and Herzegovina. Further research is needed to explore how changes in healthcare care delivery impact changes in the Chronic Care Model domain.

In the past decade, we have witnessed the emergence of a large number of different computer-based animations and simulations that have the goal to foster better learning of different physics topics. Past studies have shown many benefits of animations and simulations, but for their efficient usage it is very important that teachers are well educated in the teaching material and usage of selected visualizations. Furthermore, studies have proven that augmented reality technology has a potential to reduce cognitive load and improve the quality of physics lectures. Many of these visualizations are generally designed for targeted physics phenomena, and sometimes it is not easy to address specific students’ misconceptions. In this paper, we will present augmented reality animations and a simulation that can generally be useful for teaching about counterintuitive aspects of rolling motion, and specifically address students’ misconceptions about rolling friction and velocity in contact with the ground.

Automatic modulation recognition (AMR) technique plays an important role in the identification of modulation types of unknown signal of integrated sensing and communication (ISAC) systems. Deep neural network (DNN) based AMR is considered as a promising method. Considering the complexity of a typical ISAC system, devising the DNN manually with limited knowledge of its various classifications will be very tasking. This paper proposes a neural architecture search (NAS) based AMR method to automatically adjust the structure and parameters of DNN and find the optimal structure under the combination of training and constraints. The proposed NAS-AMR method will improve the flexibility of model search and overcome the difficulty of gradient propagation caused by the non-differentiable quantization function in the process of back propagation. Simulation results are provided to confirm that the proposed NAS-AMR method can identify the modulation types in various ISAC electromagnetic environments. Furthermore, compared with other fixed structure networks, our proposed method delivers the highest recognition accuracy, under the condition of low parameters and floating-point operations (FLOPs).

Malware traffic classification (MTC) is a very important component of cyber security, and a number of the MTC techniques are based on deep learning (DL) with a strong capability of feature mining and classification. However, these DL-based MTC methods are heavily dependent on a large amount of network traffic samples. In the few-shot scenarios, these methods usually overfit and have poor classification performance. Considering that the update cycle of malware is faster and faster, and there are more and more types of malware, collecting enough training samples for all malware is very challenging, if not impossible. In this paper, a novel few-shot MTC(FS-MTC) method is proposed based on convolutional neural network (CNN) and model-agnostic meta-learning (MAML) algorithm. Specifically, the CNN is trained on samples from normal softwares by MAML rather than the conventional optimization methods, then the CNN is finetuned by a few samples from malware for MTC. Simulation results show that our proposed MAML-based FS-MTC can outperform the traditional MTC methods. The performance of our proposed method can reach up to 95.69%.

To address the problem of spectrum resources and transmitting power for vehicular networks, this paper proposes a resource allocation (RA) method based on dueling double deep-Q network (D3QN) reinforcement learning (RL). Due to the high mobility of the vehicle, the channel changes rapidly which makes it difficult to accurately collect high-accuracy channel state information at the base station and to perform centralized management. In response of this difficulty, we construct a multi-intelligence model, using Manhattan Grid Layout City Model as the basis of environment and with each vehicle-to-vehicle (V2V) link as an intelligence. They work together to interact with the environment, receive appropriate observations, get rewards, and finally learn to improve the allocation of power and spectrum to enable users to achieve a better entertainment experience and a safer driving environment. Experimental results demonstrate that with proper training mechanism and reward function construction, cooperation among multiple intelligence can be performed in a distributed manner, with improvements in both the capacity of total vehicle-to-infrastructure links and the effective payload delivery success rate of the V2V links compared to common Q-network.

Specific emitter identification (SEI) is developed as a potential technology against attackers in cognitive radio networks and authenticate devices in Internet of Things (IoT). It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Due to the strong capability of deep learning (DL) in extracting the hidden features of data and making classification decision, deep neural networks (DNNs) have been widely used in the SEI. Considering the insufficiently labeled training dataset and large unlabeled training dataset, we propose a novel SEI method using semi-supervised (SS) learning framework, i.e., metric-adversarial training (MAT). Specifically, two object functions (i.e., cross-entropy (CE) loss combined with deep metric learning (DML) and CE loss combined with virtual adversarial training (VAT)) and an alternating optimization way are designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset. The simulation results show that the proposed method achieves a better identification performance than four latest SS-SEI methods.

The fast development of intelligent wireless communications enables many devices to access various networks. It often leads to the security risks of malicious access of illegal devices. To ensure a secure and reliable wireless access, it is necessary to identify illegal devices and prevent their attacks accurately. To improve the performance of specific emitter identification (SEI), this paper proposes a multi-scale convolution neural network (MSCNN) based on convolution layers of three branches with different convolution kernel sizes. MSCNN extracts radio frequency fingerprints (RFF) in three receptive fields through different convolution kernels. We verify the identification accuracy using the RF signals conforming to long term evolution (LTE) standard. The experimental results show that our proposed MSCNN-based SEI method can improve the absolute accuracy by 15% and the relative accuracy by 22% in perfect communication environment. In addition, we verify the robustness of proposed MSCNN by comparing identification performance in imperfect environment. Simulation results show that the proposed MSCNN can extract more hidden features through convolution kernels of different sizes, and thus achieves better SEI performance than existing methods.

Radio frequency fingerprint (RFF) is regarded as a key technology in physical layer security in various wireless communications systems. Deep learning (DL) has achieved great success in the field of signal identification, particularly in improving performance and eliminating manual feature extraction. However, the training cost of these DL-based methods is usually large. It is unwise to retrain the network with whole data when it comes to new data. Therefore, we propose a novel RFF identification method based on incremental learning (IL), which uses continuous data stream to update the identification model, constantly. Experimental results show that with the increase of increment times, the accuracy of the proposed IL-based method gradually approaches the performance of joint training, and finally reaches 96.79%, which is only 1.9% lower than the performance upper bound.

Medical radiation exposures have been reduced significantly with modern equipment and protection measures. Biomonitoring of medical personnel can provide information concerning possible effects of radiation exposure. However, chromosome aberration (CA) analysis is now recommended only when the estimated effective dose is 200 mSv or higher. In this retrospective study in Bosnia and Herzegovina, we have measured the cytogenetic status of medical workers and healthy volunteers (controls). Peripheral blood samples from 66 medical workers exposed to low-dose ionising radiation and 89 non-exposed volunteers were collected for chromosome aberrations (CA) analysis and the cytokinesis-block micronucleus (CBMN) assay. Higher rates of chromatid and chromosome breaks, acentric fragments, double minutes, micronuclei, and micronucleated binuclear cells were observed in the control group, while the rate of nucleoplasmic bridges was higher in the medical workers group.

INTRODUCTION MATERIALS AND METHODS

This paper analyzes the problem of DC cable selection in photovoltaic (PV) plants. PV plants can have tens of kilometres of one-way cables that are important parts of the system. The currents flowing through these cables can reach values of several hundred amps. Losses incurred on DC cables are up to 1%, which can be significant when measuring power loss during the operating period. Reduction of these losses can be achieved by increasing the cross-section of the cable. The paper describes the requirements set by the standards for selecting cable cross-sections. An analytical criterion function that connects electricity losses and cable crosssection were deduced. This function depends on several parameters such as electricity price, cable price, the average number of sunny hours per year, average amount of electricity through cable, interest rate, loan repayment period, and plant operation period. Several cases with the analysis of the obtained results are presented.