Factor investing has become very popular during the last decades, especially with respect to equity markets. After extending Fama–French factors to corporate bond markets, recent research more often concentrates on the government bond space and reveals that there is indeed clear empirical evidence for the existence of significant government bond factors. Voices that state the opposite refer to outdated data samples. By the documentation of rather homogeneous recent empirical evidence, this review underlines the attractiveness of more sophisticated investment approaches, which are well established in equity and even in corporate bond markets, to the segment of government bonds.

Factor investing has become very popular during the last decades, especially with respect to equity markets. After extending Fama–French factors to corporate bond markets, recent research more often concentrates on the government bond space and reveals that there is indeed clear empirical evidence for the existence of significant government bond factors. Voices that state the opposite refer to outdated data samples. By the documentation of rather homogeneous recent empirical evidence, this review underlines the attractiveness of more sophisticated investment approaches, which are well established in equity and even in corporate bond markets, to the segment of government bonds.

S U M M A R Y Inversions of planetary gravity are aimed at constraining the mass distribution within a planet or moon. In many cases, constraints on the interior structure of the planet, such as the depth of density anomalies, must be assumed a priori, to reduce the non-uniqueness inherent in gravity inversions. Here, we propose an alternative approach that embraces the non-uniqueness of gravity inversions and provides a more complete view of related uncertainties. We developed a Transdimensional Hierarchical Bayesian (THB) inversion algorithm that provides an ensemble of mass distribution models compatible with the gravitational field of the body. Using this ensemble of models instead of only one, it is possible to quantify the range of interior parameters that produce a good fit to the gravity acceleration data. To represent the interior structure of the planet or moon, we parametrize mass excess or deficits with point masses. We test this method with synthetic data and, in each test, the algorithm is able to find models that fit the gravity data of the body very well. Three of the target or test models used contain only point mass anomalies. When all the point mass anomalies in the target model produce gravity anomalies of similar magnitudes and the signals from each anomaly are well separated, the algorithm recovers the correct location, number and magnitude of the point mass anomalies. When the gravity acceleration data of a model is produced mostly by a subset of the point mass anomalies in the target model, the algorithm only recovers the dominant anomalies. The fourth target model is composed of spherical caps representing lunar mass concentration (mascons) under major impact basins. The algorithm finds the correct location of the centre of the mascons but fails to find their correct outline or shape. Although the inversion results appear less sharp than the ones obtained by classical inversion methods, our THB algorithm provides an objective way to analyse the interior of planetary bodies that includes epistemic uncertainty.

The paper presents an online web-oriented system named SOLARS, which is aimed at calculating the feasibility of building the photovoltaic (PV) systems. SOLARS currently enables potential investors to calculate the technical and financial feasibility of building the PV systems in the Republic of Srpska (Bosnia and Herzegovina). Very intuitive GUI design enables investors to obtain feasibility calculations in three simple steps: (i) selection of a geographical location, (ii) specification of technical parameters, and (iii) specification of financial parameters. A usage scenario is illustrated by a real feasibility calculation example.

The effective collection and management of personal data of rapidly migrating populations is important for ensuring adequate healthcare and monitoring of a displaced peoples' health status. With developments in ICT data sharing capabilities, electronic personal health records (ePHRs) are increasingly replacing less transportable paper records. ePHRs offer further advantages of improving accuracy and completeness of information and seem tailored for rapidly displaced and mobile populations. Various emerging initiatives in Europe are seeking to develop migrant-centric ePHR responses. This paper highlights their importance and benefits, but also identifies a number of significant ethical, legal and social issues (ELSI) and challenges to their design and implementation, regarding (1) the kind of information that should be stored, (2) who should have access to information, and (3) potential misuse of information. These challenges need to be urgently addressed to make possible the beneficial use of ePHRs for vulnerable migrants in Europe.

The rapid development of financial markets results in data variability and unpredictability. Anomaly detection in financial data is a very important issue. Finding anomalies can result in error reduction and corrections in due time. The main aim of this research was to find anomalies in general ledgers of a real company in Bosnia and Herzegovina. Anomalies are defined as input errors of accountants. Main concepts of anomaly detection are defined, a summary of the current progress is given, and challenges of future work are presented. Cluster-based and histogram-based anomaly detections were performed on a real-life dataset of a microcredit organization. Results of algorithms were presented, as well as results achieved using synthetic data.

Postural orthostatic tachycardia syndrome (POTS) is a chronic, debilitating condition characterized by heterogeneous symptoms, such as lightheadedness, palpitations, pre-syncope, syncope, and weakness or heaviness, especially of the legs. It is frequently associated with hypermobile joints or conditions such as chronic fatigue syndrome, chronic abdominal pain, migraine headache, and diabetes mellitus. Described is a case of POTS, which though it is not rare, is rarely diagnosed. It can be diagnosed quickly with simple methods.

Abstract The main objective of this investigation was to determine and summarize the economic burden of severe COPD exacerbations that required hospitalization and the difference in the costs of treatment between patients with frequent (at least two exacerbations in one year) and infrequent exacerbation. Our results suggested that significantly more resources had to be spent to treat patients with at least two hospitalizations during the study related to the use of medications primarily affecting the respiratory system (corticosteroids, p = 0.013, theophylline, p = 0.007) and total hospital stay (31336.68 ± 19140 RSD/517.53 ± 316.1 EUR versus 23650.15 ± 14956.0 RSD/390.59 ± 247 EUR, p=0.002) compared to patients who stayed in a semi-intensive care unit (12875.35 ± 20742.54 RSD versus 4310.62 ± 9779.78 RSD/ 212.64 ± 342.57 EUR versus 71.19 ± 161.51 EUR, p=0.006). Based on the total number of days in the hospital, the costs of the drugs, the materials used and services provided, patients from the frequent exacerbation group had significantly higher costs (80034.1 ± 36823.7 RSD/1321.78 ± 608.15 EUR versus 69425.5 ± 34083.1 RSD/1146.58 ± 562.89 EUR) comparedthan patients in the infrequent exacerbation group (p=0.039). Our results indicate that significantly more funds will be spent treating the deterioration of patients who stay longer in the hospital or in the semi-intensive care unit. Their condition will require a significantly greater use of drugs that are primarily used to treat the respiratory system and, therefore, will utiliseutilize significantly more resources.

The highly dynamic wireless communication environment poses a challenge for many applications (e.g., adaptive multimedia streaming services). Providing accurate TP can significantly improve performance of these applications. The scheduling algorithms in cellular networks consider various PHY metrics, (e.g., CQI) and throughput history when assigning resources for each user. This article explains how AI can be leveraged for accurate TP in cellular networks using PHY and application layer metrics. We present key architectural components and implementation options, illustrating their advantages and limitations. We also highlight key design choices and investigate their impact on prediction accuracy using real data. We believe this is the first study that examines the impact of integrating network-level data and applying a deep learning technique (on PHY and application data) for TP in cellular systems. Using video streaming as a use case, we illustrate how accurate TP improves the end user's QoE. Furthermore, we identify open questions and research challenges in the area of AI-driven TP. Finally, we report on lessons learned and provide conclusions that we believe will be useful to network practitioners seeking to apply AI.

The cultural heritage image classification represents one of the most important tasks in the process of digitalization. In this paper, a deep learning neural network was applied in order to classify images of architectural heritage belonging to ten categories, in particular: (i) bell tower, (ii) stained glass, (iii) vault, (iv) column, (v) outer dome, (vi) altar, (vii) apse, (viii) inner dome, (ix) flying buttress, and (x) gargoyle. The Convolutional neural network was used for image classification, with the same architecture applied on two sets of the data: the full dataset consisting of 10 categories as well as dataset with 5 different image categories. The results show that both architectures performed well and obtained accuracy of up to 90%.