Cellular respiration is a pathway that uses energy from food molecules and oxygen for different processes of life such as movement. Oxygen, transported through mass transport from blood vessels to skeletal muscle can bind to myoglobin that stores a small amount of oxygen or can be used in cellular respiration by mitochondria. This paper presents a Simulink model of oxygen distribution in skeletal muscle, based on previously published mathematical models. Different parameters for supply concentration of oxygen at capillary wall and consumption rate of the muscle tissue simulate oxygen distribution when the body is under certain conditions: in rest state, dysoxia and exercise state.

The deployment of deep neural network (DNN) models in software applications is increasing rapidly with the exponential growth of artificial intelligence. Currently, such models are deployed manually by developers in the cloud considering several user requirements, while the decision of model selection and user assignment is difficult to take. With the rise of edge computing paradigm, companies tend to deploy applications as close as possible to the user. Considering this system, the problem of DNN model selection and the inference serving becomes harder due to the introduction of communication latency between nodes. We present an automatic method for DNN placement and inference in edge computing; a mathematical formulation to the DNN Model Variant Selection and Placement (MVSP) problem is presented, it considers the inference latency of different model-variants, communication latency between nodes, and utilization cost of edge computing nodes. Furthermore, we propose a general heuristic algorithm to solve the MVSP problem. We provide an analysis of the effects of hardware sharing on inference latency, on an example of GPU edge computing nodes shared between different DNN model-variants. We evaluate our model numerically, and show the potentials of GPU sharing, with decreased average latency by 33% of millisecond-scale per request for low load, and by 21% for high load. We study the tradeoff between latency and cost and show the pareto optimal curves. Finally, we compare the optimal solution with the proposed heuristic and showed that the average latency per request increased by more than 60%. This can be improved using more efficient placement algorithms.

Algorithms for solving Rubik’s cube have been an active research area since the first appearance of the cube in 1974. The challenge posed when solving the cube is to choose an algorithm that solves the cube for the minimum number of steps. Many algorithms are already implemented in software, but not many are tested with modern hardware-software methodologies, such as hardware-software co-design. Here, the challenge is to take into consideration limiting factors of hardware and implement the most efficient solution. In this paper, the hardware/software co-design is used to solve the random configuration of Rubik’s cube. Two algorithms are used: the Basic algorithm and the Kociemba algorithm. The Basic algorithm is easy to understand and implement but requires many more steps to solve the cube than the Kociemba algorithm. The Kociemba algorithm requires some pre-processing tasks, such as depth-first search and pruning trees, but can solve the cube in about 25 moves. Both algorithms are implemented and tested on a custom-made robot with mechanical parts, actuators, grippers and Intel’s DE1-SoC for drive control and implementation of solving algorithms. The robot successfully solved a number of random configurations. Performances (running time, number of moves needed for solving the cube) of both algorithms are measured and compared.

Rift Valley fever (RVF) is an arboviral zoonosis that primarily affects ruminants but can also cause illness in humans. The increasing impact of RVF in Africa and Middle East and the risk of expansion to other areas such as Europe, where competent mosquitos are already established, require the implementation of efficient surveillance programs in animal populations. For that, it is pivotal to regularly assess the performance of existing diagnostic tests and to evaluate the capacity of veterinary labs of endemic and non-endemic countries to detect the infection in an accurate and timely manner. In this context, the animal virology network of the MediLabSecure project organized between October 2016 and March 2017 an external quality assessment (EQA) to evaluate the RVF diagnostic capacities of beneficiary veterinary labs. This EQA was conceived as the last step of a training curriculum that included 2 diagnostic workshops that were organized by INIA-CISA (Spain) in 2015 and 2016. Seventeen veterinary diagnostic labs from 17 countries in the Mediterranean and Black Sea regions participated in this EQA. The exercise consisted of two panels of samples for molecular and serological detection of the virus. The laboratories were also provided with positive controls and all the kits and reagents necessary to perform the recommended diagnostic techniques. All the labs were able to apply the different protocols and to provide the results on time. The performance was good in the molecular panel with 70.6% of participants reporting 100% correct results, and excellent in the serological panel with 100% correct results reported by 94.1% of the labs. This EQA provided a good overview of the RVFV diagnostic capacities of the involved labs and demonstrated that most of them were able to correctly identify the virus genome and antibodies in different animal samples.

The problem of counterfeiting diplomas in education with the advancement of digital technology is increasingly pronounced. The process of forging documents is almost always accompanied by reduced transparency in issuing the documents with no possibility to easily check the validity of the document. One of the currently very attractive and challenging technology in digitized society is the blockchain technology and all the sequential systems that have emerged based on it. One such system is the Ethereum platform, which uses blockchain technology and enables the creation of decentralized applications programmed to run on the Ethereum network. One of the Ethereum use is a Smart Contract, which allows applications to be executed online, completely autonomously without the influence of a third party, once a previously defined condition is satisfied. The objective of this research is to explore the possibility of using a Smart Contract in the process of the creation and issuance of diplomas at a higher education institution. At the end of the analysis, we provide an overview of the advantages and disadvantages of this procedure, as well as potential possibilities for its improvements. The possibilities of automation and the cost of such a process were also considered.

In this paper, empirical research about Passenger Car Equivalents (PCEs) on the longitudinal downgrade of two-lane roads in Bosnia and Herzegovina has been conducted in order to determine the influence of vehicle structure under free traffic flow conditions. The research has been carried out considering the classes of vehicles at cross-sections on the downgrade of two-lane roads. As a result, the negative influence of vehicle structure under free traffic flow conditions using passenger car equivalents (PCEs) has been determined. The results show that on the downgrade of two-lane roads, the value of passenger car equivalent decreases from the level terrain to the boundary minimum value for the determined downgrade g = −3.00%, after which its value starts to increase slightly. Based on the obtained values, the models calibrated with a second-degree polynomial have been developed to determine the average value of passenger car equivalent as a function of its boundary value. The paper also compares the results obtained by the developed models with the models from the Highway Capacity Manual under free traffic flow conditions. In addition, models for the percentage values of PCE15%, PCE50% and PCE85% have been established.

Clinical mistreatment and mismanagement are big issues caused by detection of too many false negative patients. Therefore, lung cancer diagnostic inaccuracy and methods to surpass it in a minimally invasive way is often the subject of research, as it is case of this study. This study focuses on the use of machine learning algorithms as a noninvasive tool to differentiate malignant pleural effusions from benign effusions. It provides performance comparisons between Adaptive neuro-fuzzy inference system (ANFIS), Support vector machine (SVM), RUS Boosted Tree (RUSBoost) and K-Nearest-Neighbor (K-NN) techniques for lung cancer detection. The proposed algorithms were chosen based on the current state of the art in the field of pulmonary diagnostics. The novelty of this work is the application of machine learning models for classification of lung cancer based on expression of tumor markers obtained from serum and pleural fluids. The performance of all models is compared and validated on data samples of 168 patients. Three classification model, SVM, RUSBoost and K-NN performed equally well, whereas underperforming model was ANFIS.

This paper explores the new way of presenting one existing VR application, which was described in our previous work - Virtual Reality Experience of Sarajevo War Heritage. The goal of the application was to introduce more people with the Sarajevo siege and allow them to experience the Tunnel crossing at that time. Before this application, we made two versions, the first one for VR setup and the second for the web. In this paper, we introduce a mobile version with the same content. The challenge was to optimize the content for the mobile experience. The assets were optimized so a wider number of mobile phones with different hardware capabilities can run the application. The advantages and disadvantages of this approach are pointed out, and the limitations of the mobile application are emphasized. The memory usage and frame rate are measured for different Android devices with different operating system versions and hardware capabilities. The results show the optimized application can be run on different Android mobile devices. Nevertheless, for better user experience a higher number of frames per second is needed, which may include reducing the quality of the assets.

Distribution companies often store goods in large warehouses. Orders are collected and prepared for transport. Large-scale warehouses are often divided into sectors. Each worker collects a part of the order from the assigned sector. In that case, workers often pick small orders and the process is not optimal. Therefore, order batching is done, where one worker collects multiple orders at a time. In this paper, an innovative concept of orders batching in a warehouse with a 48-hour delivery based on a metaheuristic approach is described. The algorithm divides each order by sectors. An analysis of each part of the order is done and the possibility of batching based on the order content is checked. The order batching is based on the discrete Bat algorithm. The transport scheme and the order of loading goods into the truck are observed. In the order picking process, a number of standard constraints such as weight and item priorities are considered. The concept has been implemented and tested for 50 days of warehouse operation in one of the largest warehouses in Bosnia and Herzegovina. The algorithm is compared with the earlier approach of collecting orders in the warehouse, and significant progress has been observed in the number of kilometers traveled on a daily basis.

The convergence of quantum cryptography with applications used in everyday life is a topic drawing attention from the industrial and academic worlds. The development of quantum electronics has led to the practical achievement of quantum devices that are already available on the market and waiting for their first application on a broader scale. A major aspect of quantum cryptography is the methodology of Quantum Key Distribution (QKD), which is used to generate and distribute symmetric cryptographic keys between two geographically separate users using the principles of quantum physics. In previous years, several successful QKD networks have been created to test the implementation and interoperability of different practical solutions. This article surveys previously applied methods, showing techniques for deploying QKD networks and current challenges of QKD networking. Unlike studies focusing on optical channels and optical equipment, this survey focuses on the network aspect by considering network organization, routing and signaling protocols, simulation techniques, and a software-defined QKD networking approach.

The rapid development of technology is directly affecting the growth and development of e-commerce shipments, especially in the Business to Customer segment. An increase in e-commerce shipments has a strong impact on the express delivery industry. In these conditions, a very significant challenge is how to organize a postal network. The problem that arises is how many postal centers, and at what locations, should be implemented in a specific geographical area in order to optimize the level of service for the users. Solving this challenge has latterly received increased attention in both industry and academia. The aim of this paper is to firstly provide a concise overview of current approaches in the process of determining the optimal location of postal centers. The second part of the paper will focus on proposing an approach that will rely on machine learning methods for clustering in defined conditions and specific geographical environment using appropriate geographic information tools for spatial data analysis and visualization.

Blockchain technology apparently is a trivial innovation, but this technology has attracted huge investors in a very short period compared to other technologies, and it is still having a lot of potential applications. Smart contracts are making possible execution in an automated and safe way by using blockchain technology. Therefore, smart contracts are applied in this research for the expert system. This paper is about an expert system working with smart contracts and neural networks as the inference machine to decide on the sensors optimal distribution and taking actions when sensor readings are out of range: control lights, activating fire alarms, temperature alarms, etc. for all spaces (parks, schools, hospitals, etc.) in a smart city based on the needs, and likes of the expert system user. This expert system works using a blockchain structure on the EOSIO ecosystem with all data gathered by the sensors being saved in cloud online making internet of things environment and essential data saved in a blockchain node.

We consider the problem of the choice of gauge in nonrelativistic strong-laser-field physics. For this purpose, we use the phase-space path-integral formalism to obtain the momentum-space matrix element of the exact time-evolution operator. With the assumption that the physical transition amplitude corresponds to transitions between eigenstates of the physical energy operator rather than the unperturbed Hamiltonian H0=(−i∂/∂r)2/2+V(r), we prove that the aforementioned momentum-space matrix elements obtained in velocity gauge and length gauge are equal. These results are applied to laser-assisted electron-ion radiative recombination (LAR). The transition amplitude comes out identical in length gauge and velocity gauge, and the expression agrees with the one conventionally obtained in length gauge. In addition to the strong-field approximation (SFA), which is the zeroth-order term of our expansion, we present explicit results for the first-order and the second-order terms, which correspond to LAR preceded by single and double scattering, respectively. Our general conclusion is that in applications to atomic processes in strong-field physics the length-gauge version of the SFA (and its higher-order corrections) should be used. Using the energy operator as the basis-defining Hamiltonian, we have shown that the resulting transition amplitude is gauge invariant and agrees with the form commonly derived in length gauge.

Riverine nutrient loads are among the major causes of eutrophication of the Baltic Sea. This study applied the Soil & Water Assessment Tool (SWAT) in three catchments flowing to the Baltic Sea, namely Vantaanjoki (Finland), Fyrisån (Sweden), and Słupia (Poland), to simulate the effectiveness of nutrient control measures included in the EU’s Water Framework Directive River Basin Management Plans (RBMPs). Moreover, we identified similar, coastal, middle-sized catchments to which conclusions from this study could be applicable. The first modelling scenario based on extrapolation of the existing trends affected the modelled nutrient loads by less than 5%. In the second scenario, measures included in RBMPs showed variable effectiveness, ranging from negligible for Słupia to 28% total P load reduction in Vantaanjoki. Adding spatially targeted measures to RBMPs (third scenario) would considerably improve their effectiveness in all three catchments for both total N and P, suggesting a need to adopt targeting more widely in the Baltic Sea countries.