In this paper, we present our work in close-distance non-verbal communication with tabletop robot Haru through hand gestural interaction. We implemented a novel hand gestural understanding system by training a machine-learning architecture for real-time hand gesture recognition with the Leap Motion. The proposed system is activated based on the velocity of a user's palm and index finger movement, and subsequently labels the detected movement segments under an early classification scheme. Our system is able to combine multiple gesture labels for recognition of consecutive gestures without clear movement boundaries. System evaluation is conducted on data simulating real human-robot interaction conditions, taking into account relevant performance variables such as movement style, timing and posture. Our results show robustness in hand gesture classification performance under variant conditions. We furthermore examine system behavior under sequential data input, paving the way towards seamless and natural real-time close-distance hand-gestural communication in the future.

Background: Oral premalignant lesions (OPLs) represent the most common oral precancerous conditions. One of the major challenges in this field is the identification of OPLs at higher risk for oral squamous cell cancer (OSCC) development, by discovering molecular pathways deregulated in the early steps of malignant transformation. Analysis of deregulated levels of single genes and pathways has been successfully applied to head and neck squamous cell cancers (HNSCC) and OSCC with prognostic/predictive implications. Exploiting the availability of gene expression profile and clinical follow-up information of a well-characterized cohort of OPL patients, we aim to dissect tissue OPL gene expression to identify molecular clusters/signatures associated with oral cancer free survival (OCFS). Materials and methods: The gene expression data of 86 OPL patients were challenged with: an HNSCC specific 6 molecular subtypes model (Immune related: HPV related, Defense Response and Immunoreactive; Mesenchymal, Hypoxia and Classical); one OSCC-specific signature (13 genes); two metabolism-related signatures (3 genes and signatures raised from 6 metabolic pathways associated with prognosis in HNSCC and OSCC, respectively); a hypoxia gene signature. The molecular stratification and high versus low expression of the signatures were correlated with OCFS by Kaplan–Meier analyses. The association of gene expression profiles among the tested biological models and clinical covariates was tested through variance partition analysis. Results: Patients with Mesenchymal, Hypoxia and Classical clusters showed an higher risk of malignant transformation in comparison with immune-related ones (log-rank test, p = 0.0052) and they expressed four enriched hallmarks: “TGF beta signaling” “angiogenesis”, “unfolded protein response”, “apical junction”. Overall, 54 cases entered in the immune related clusters, while the remaining 32 cases belonged to the other clusters. No other signatures showed association with OCFS. Our variance partition analysis proved that clinical and molecular features are able to explain only 21% of gene expression data variability, while the remaining 79% refers to residuals independent of known parameters. Conclusions: Applying the existing signatures derived from HNSCC to OPL, we identified only a protective effect for immune-related signatures. Other gene expression profiles derived from overt cancers were not able to identify the risk of malignant transformation, possibly because they are linked to later stages of cancer progression. The availability of a new well-characterized set of OPL patients and further research is needed to improve the identification of adequate prognosticators in OPLs.

The paper presents an approach aimed at automatically deriving the conceptual database model from a set of business process models. The approach proposes the incremental synthesis of the target model by iteratively composing the partial conceptual database models that are derived from the models contained in the source set. The approach is implemented by the AMADEOS tool, which is the first online web-based tool enabling the automatic derivation of the conceptual database model from a set of business process models.

The combination of 5G and Multi-access Edge Computing (MEC) technologies can bring significant benefits to vehicular networks, providing means for achieving enhanced Quality of Service (QoS), and Quality of Experience (QoE) of wide variety of vehicular applications. Although beneficial in terms of latency reduction, the edge of the architecture for communication networks produces enormous heterogeneity of network services and resources. This challenge becomes even more severe when different administration domains are taken into consideration. Thus, efficient network Management and Orchestration (MANO) of network resources and services are inevitable. As ETSI provided guidelines and standardization for NFV MANO components, the MEC platform can be used to host network services, while MANO systems are in charge of network service management and orchestration. In this paper, we focus on the specific impact that the Virtualized Infrastructure Manager (VIM) has on the performance of the whole MANO system, used for management and orchestration of MEC services and resources in vehicular networks by enabling the on-demand service instantiation, and service teardown. In our testbed-based evaluation, we measured the network service instantiation and termination delays when evaluating: a) OpenStack and Amazon Web Services (AWS) as VIMs for Open Source MANO (OSM), and b) OpenStack and Docker in case of Open Baton. Such performance analysis with a strong experimental component can serve as a baseline for researchers and industry towards exploiting the opportunities that existing MANO solutions provide.

This paper discusses the problem of powering a remote rural mobile base station using a standalone hybrid renewable energy system. A wind turbine and photovoltaic system are employed as the complementary power generation technologies, while the diesel generator serves as a backup power supply. A battery is required to reduce the impact of intermittency of renewable sources. On the consumption side, along with telecommunication electronic equipment, the consumption of cooling devices as a result of the ambient temperature, is also taken into account. The behavior of the base station in electrical and thermal terms is tested using the sequential Monte Carlo simulation. Adequate models have been used to generate wind, irradiance, and temperature input series, using the monthly averages for calibration, as the statistic information that is widely available in meteorological atlases, even for remote rural locations. The developed software provides all the variables of interest either in the form of chronological diagrams or probability histograms. The simulation platform can also be incorporated as a module of an algorithm for selection of optimal capacity of the generating system elements and for the optimal control of the cooling devices.

Condition monitoring is a fundamental technology that enables predictive maintenance of automation systems. However, as automation systems increase in complexity, the development of condition monitoring software becomes a challenging task that requires extensive knowledge from multiple engineering disciplines. In this context, the identification and specification of condition monitoring software requirements play a key role. Neglecting these tasks often results in costly problems during later stages of systems development. Currently, means to support interdisciplinary requirements engineering within condition monitoring software development are missing. In particular, there is a need for a systematic process that supports modeling condition monitoring requirements. In this paper, we present our solution - a profile based on the extension of the SysML, which is commonly used to engineer requirements in automation systems. The profile allows specification of condition monitoring software requirements and thus enables a more domain-specific requirements engineering approach. We illustrate this approach on a heat exchanger condition monitoring system, explain the particular modeling steps, and present lessons learned.

Human coronaviruses, especially SARS-CoV-2, are emerging pandemic infectious diseases with high morbidity and mortality in certain group of patients. In general, SARS-CoV-2 causes symptoms ranging from the common cold to severe conditions accompanied by lung injury, acute respiratory distress syndrome in addition to other organs’ destruction. The main impact upon SARS-CoV-2 infection is damage to alveolar and acute respiratory failure. Thus, lung cancer patients are identified as a particularly high-risk group for SARS-CoV-2 infection and its complications. On the other hand, it has been reported that SARS-CoV-2 spike (S) protein binds to angiotensin-converting enzyme 2 (ACE-2), that promotes cellular entry of this virus in concert with host proteases, principally transmembrane serine protease 2 (TMPRSS2). Today, there are no vaccines and/or effective drugs against the SARS-CoV-2 coronavirus. Thus, manipulation of key entry genes of this virus especially in lung cancer patients could be one of the best approaches to manage SARS-CoV-2 infection in this group of patients. We herein provide a comprehensive and up-to-date overview of the role of ACE-2 and TMPRSS2 genes, as key entry elements as well as therapeutic targets for SARS-CoV-2 infection, which can help to better understand the applications and capacities of various remedial approaches for infected individuals, especially those with lung cancer.

The coexistence of humans and dogs, in addition to all positive effects, can result in negative effects on human health. A particular risk is posed by a population of stray dogs, that is, dogs without owners and veterinary supervision. A contact with dogs in addition to bites, carries the risk of viral, bacterial and parasitic zoonoses, and can also cause psychological trauma. Children, the elderly and pregnant women are the categories most susceptible to the negative effects of dogs. The aim of the paper was to make an interdisciplinary analysis of the negative effects of dogs on humans. Dog bites cause wounds and dysfunction of damaged tissue, and often lead to various infections. The risks of rabies and tetanus are particularly significant if proper and timely treatment is not performed. Ongoing training for dog owners can significantly reduce the number of bites inflicted by owned dogs, but stray dogs remain a serious social problem and pose potential health risks of some zoonosis. Timely and adequate management of bite wounds and the use of rabies-post-exposure prophylaxis as well as psycho-therapy, where indicated, significantly reduce possible adverse health effects for patients who have been bitten by dogs.

Aim To investigate clinical and obstetrical characteristics, an outcome and a prognosis for pregnant women with diagnosed and treated genital or extragenital cancer and their newborns. Methods This retrospective cohort study included pregnant and childbearing women with a history of cancer diagnosed before pregnancy during the period between 1 January 2014 and 31 December 2018. Data related to the course of pregnancy and childbirth were collected from medical records (mothers' disease history and partogram). The analysis covered clinical and histopathological characteristics of cancers, type of the treatment (surgery, chemotherapy, radiotherapy), demographic data, obstetric characteristics, comorbidities of women, and outcome of the newborns. Results The study recorded 18 414 deliveries, of which 30 (0.16%) were pregnancies in women who had been diagnosed and treated earlier for genital or extragenital cancer. The average age of the women at the time of delivery was 29.43±5.97 years. There were six (20%) women with genital and 24 (80%) with extragenital cancer. The most frequent extra genital cancer was Hodgkin lymphoma, in eight (26.6%) cases; ovarian cancer was the most frequent genital cancer, in four (13.3%) cases. The average time span from the cancer diagnosis and start of the treatment to the delivery was 59.2±44.4 months (5 years) (range 12 months - 15 years). Two (6.6%) women died. Conclusion Our data demonstrate a favourable obstetric and neonatal outcome for women who have survived cancer.

Aim To compare maternal, foetal and neonatal characteristics, and perinatal outcome of preterm and term deliveries in twins pregnancies in order to improve perinatal care in Bosnia and Herzegovina. Methods This retrospective cohort study included pregnant women with twin pregnancy who delivered during the period between 1 January 2012 and 31 December 2018 at the Clinic for Gynaecology and Obstetrics, University Clinical Centre Tuzla. Results During the seven-year period 26 734 deliveries were recorded, out of which 362 (1.35 %) were twin pregnancies, 226 (62.4%) preterm and 136 (37.5%) term ones. In the preterm group 38 (16.8%) pregnancies were assisted medical reproduction, and 16 (11.7%) of those were in the term group. The average birth weight was significantly higher for the first twin in both groups (p<0.00001). Incipient intrauterine foetal asphyxia was more frequent in the preterm group (p<0.05). The most common indication for Caesarean section was abnormalities of foetal presentation and lie, 176 (68.2%) for the overall sample. Conclusion Cornerstone of twin pregnancy antenatal care is to get correct data about amnionicity and chorionicity. Since majority of prenatal data did not have this information we call all obstetricians to declare about amnionicity and chorionicity in twin pregnancies during the first trimester ultrasound examination.

This paper aims to examine the impact of digital tools in mathematics and the readiness of teachers and students applying these interactive tools in teaching. The data used in the research are obtained from the test results of 526 students, in five secondary schools in North Macedonia. The students in this research, are divided into two groups: mainly as a control group and an experimental group. The control group is the group of students who do not have access to the interactive tools at home and who use interactive tools only once a week in the school while the group of students in the experimental group have access to them and have the opportunity to use these interactive applications every day. The students in the control group and the experimental group were selected from the same year and the gender equality of the groups was taken into account. To further understand the relationship between teaching with digital tools and learning after testing was surveyed the participants. The results in our research suggest that interactive teaching tools have a positive impact on the teaching process and increase students' knowledge.

Introduction: Ventilator associated pneumonia (VAP) is defined as nosocomial pneumonia in patients who have mechanical ventilation (MV) for more than 48 hours. The diagnosis of VAP is based on radiological-microbiological examinations. In the United States, the Centers for Disease Control and Prevention (CDC) and the National Health Care Network (NHSN) have an incidence of VAP of 5.8% per 1,000 days on mechanical ventilator. Aim: In this study, we had an aim to determine the occurrence of ventilator-associated pneumonia (VAP) in patients with MV who were hospitalized in the intensive care unit. Method: The study was retrospective, clinical, conducted in the period from January 1, 2016 until December 31, 2016. In a one-year period, 719 patients of both sex, aged 14 to 91, were hospitalized in the intensive care unit of the Clinic for Anesthesia and Resuscitation of the University Clinical Center in Sarajevo. The study included 250 patients of both sex who had respiratory support with mechanical ventilator. No patient was excluded from the study. As a confirmation of VAP, we used microbiological reports from the patient history documentation. The results were presented statistically through tables and graphs, numerically, by a percentage, and by a mean value with standard deviation. Results: Out of the 719 hospitalized patients, 250 or 34.8% underwent controlled ventilation. In 103 or 41.2% of patients some form of pneumonia was confirmed microbiologically. An average patient age on controlled ventilation was 60.4 ± 16.8 years. The mean age of a female patients who were on controlled ventilation was 63.2 ± 16.7, higher than that of male patients, which was 57.8 ± 16.6 years. The most frequent patients were over 60 years of age (52.8%). The shortest hospitalization of patients on controlled mechanical ventilation was 1 day and the longest was 120 days. Average duration of mechanical ventilation was 6.9 ± 10.5 days. Conclusion: VAP is a relatively common complication in patients with MV that can increase morbidity and mortality, as well as treatment costs. It is more frequent in females and in the elderly. Medical staff should provide normal maintenance of respiratory functions to a patient who is on MV, which will reduce the risk of VAP.