Internet of Things (IoT) becomes an emerging network technology that expedites billions of devices to be connected via the Internet to provide real-time intelligent application services. The benefits of Software-Defined Networking (SDN) can be used to fulfill IoT requirements. Quality of Service provisioning is an on-going demand in software-defined IoT (SD-IoT), particularly for large scale environments. In this paper, we address this issue by proposing a seamless model of AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in distributed fragmentation optimized SDN-IoT. Firstly, we propose a Multi-Criterion based Deep Packet Inspection method for classifying the network traffic, which is held in Edge Routers (access points). Secondly, we construct a Partially Connected Network Topology using the ISOMAP algorithm for an effective rule placement and routing. We propose a Traffic Differentiated QoS Routing for forwarding data packets via the most suitable switches. We select the optimum route by Deep Alternative Neural Network (DANN). Based on the relationships among switches, the path is selected and flow rules are deployed. The poor QoS is often caused by load imbalance in controllers and switches. To overwhelm this issue, we propose a Dynamic Offloading scheme in SD-IoT. We offload the data packets from the overloaded controller to the underloaded controller using Hassanat Distance-based K-nearest neighbors (HDK-NN) algorithm. Similarly, we propose a Ranking-based Entropy function (R-Ef) to allow dynamic offloading among switches. Simulation is performed using the NS3.26 simulator and the results proved that our proposed AI-aided SD-IoT model provides superior QoS performance compared to previous approaches.

While the genetic evolutionary features of solid tumour growth are becoming increasingly described, the spatial and physical nature of subclonal growth remains unclear. Here we utilise 102 macroscopic whole tumour images from clear cell renal cell carcinoma (ccRCC) patients, with matched genetic and phenotypic data from 756 biopsies. Utilising a digital image processing pipeline the boundaries between tumour and normal tissue were marked by a renal pathologist, and positions of boundary line and biopsy regions were extracted to X- and Y-coordinates. The coordinates were then integrated with genomic data to map exact spatial subclone locations, revealing how genetically distinct subclones grow and evolve spatially. A phenotype of advanced and more aggressive subclonal growth was present in the tumour centre, characterised by an elevated burden of somatic copy number alterations, higher necrosis, proliferation rate and Fuhrman grade. Moreover, metastasising subclones were found to preferentially originate from the tumour centre. Collectively these observations suggest a model of accelerated evolution in the tumour interior, with harsh hypoxic environmental conditions leading to heightened cellular turnover and greater opportunity for driver SCNAs to arise and expand due to selective advantage. Tumour subclone growth was found to be predominantly spatially contiguous in nature, with subclone dispersal a rare event found in two cases, which notably was associated with metastasis. In terms of genetic events, the largest subclones spatially were dominated by driver somatic copy number alterations, suggesting a large selective advantage can be conferred to subclones upon acquisition of these alterations. In conclusion, spatial dynamics is strongly associated with genomic alterations and plays an important role in tumour evolution.

Introduction: Mathematical modeling of coronavirus disease spread and computer simulations are currently one of the main tools in public health that can give important indicators for prevention planning. Based on mathematical projections and daily updates of information, the measures are either tightened or reduced, in order to protect the health of the population. Aim: The aim of this paper is to present a computer system based on an adequate mathematical model that allows frequent execution of various scenarios of spread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in any period in the future. Also, the aim of this article is to point out the importance of measures for the prevention of coronavirus disease 2019 (COVID-19) in Bosnia through examples of computer simulation models. Methods: Software solution based on the USLIRD model (Unpopulated - Susceptible - Latent - Infectious - Recovered - Deceased) was developed, with a number of variable parameters ‘reproduction number, delay period, infectivity period, hospital capacity, characteristics of population). By setting these parameters in accordance with the existing and available data, the model is brought to an optimized state with the possibility of a realistic assessment of the course of the infection curve in any future period. Data from the beginning of the pandemic are collected at the Faculty of Mechanical Engineering, University of Sarajevo and updated several times a day. The set of measures is divided into two types. 'Intervention 1' is a measure to close institutions that are at high risk for pandemics, working from home, wearing face mask, enhanced hygiene when entering facilities with a larger number of people. 'Intervention 2' presents restrictive measures that has been introduced as mandatory in Bosnia. The period 01.03.2020 to 01.09.2020 was observed. Results: Without epidemiological measures, Bosnia's health system would quickly collapse. Restrictive measures reduce the intensity of the spread of the infection, save human lives and keep the health system functional, but with consequences on other aspects of society - reduction of economic activities, collapse of the service industry and companies and disorders in mental health status of the population. Four different scenarios of the situation were analyzed. Scenario number three is current condition with measures that are currently in Bosnia. The reintroduction of restrictive measures leads to a decrease in the number of infected population and suppression of the spread of the pandemic, which is shown in scenario 4. Conclusion: Self-discipline, adherence to measures, while trying to avoid restrictive measures should be the way to fight the COVID-19 pandemic. Whatever the consequences, the initiation of restrictive measures to preserve the health of the population should be imperative.

This paper describes an example of an explainable AI (Artificial Intelligence) (XAI) in a form of Predictive Maintenance (PdM) scenario for manufacturing. Predictive maintenance has the potential of saving a lot of money by reducing and predicting machine breakdown. In this case study we work with generalized data to show how this scenario could look like with real production data. For this purpose, we created and evaluated a machine learning model based on a highly efficient gradient boosting decision tree in order to predict machine errors or tool failures. Although the case study is strictly experimental, we can conclude that explainable AI in form of focused analytic and reliable prediction model can reasonably contribute to prediction of maintenance tasks.

A set of nine Simple Sequence Repeat (SSR) loci, approved by the ECPGR Prunus working group, are proposed as a standard set for genotyping European plum accessions. These loci show sufficient reliability in spite of problems caused by hexaploidy. Polymorphism in the loci is high and enables differentiation between unique plum accessions as well as analyses of genetic grouping and overall genetic structure. A set of seven reference accessions are described. A compiled dataset with allelic information for 165 accessions is presented. Genetic structure reveals three different K-values (2, 4 and 9) demonstrating a major dichotomy between Prunus insititia-related accessions and cultivars belonging to Prunus domestica sensu stricto, as well as differentiation among minor subgroups defined by pomological traits and geographical origin.

We studied the feasibility of increasing the possible number of BCI commands that can be produced through our EEG-fTCD BCI by solving the 3-class problems of both the MI and flickering MR/WG paradigms.

The work here proposed aims at presenting the legal perspectives provided in the law of administrative improbity (Law. 8,429 / 92), especially with regard to the applicability of the necessary review in cases of impropriety handed down in actions of such nature.  It is important to remember that the necessary review has no legal provision in Law 8,429 / 92. However, there is discussion in the doctrinal and jurisprudential scope regarding the application of the institute in actions of administrative improbity with an unfounded sentence. At first, the paper seeks to present some aspects of the Administrative Improbity Law. Therefore, in brief lines, it will explain about the purpose of the necessary review and, finally, it will demonstrate the disagreements about the application or not of the actions resulting from administrative improbity.

BACKGROUND Posterior communicating (Pcom) aneurysms in the modern era have tended toward increased complexity and technical difficulties. The pretemporal approach is a valuable extension to the pterional approach for basilar apex aneurysms, but its advantages for Pcom aneurysms have not been previously elucidated. OBJECTIVE To quantify characteristics of the pretemporal approach to the Pcom. METHODS We dissected 6 cadaveric heads (12 sides) with a pretemporal transclinoidal approach and measured the following variables: (1) exposed length of internal carotid artery (ICA) proximal to the Pcom artery; (2) exposed circumference of ICA at the origin of Pcom; (3) deep working area between the optic nerve and tentorium/oculomotor nerve; (4) superficial working area; (5) exposure depth; and (6) the frontotemporal (superior posterolateral) and (7) orbito-sphenoidal (inferior anterolateral) angles of exposure. RESULTS Compared with pterional craniotomy, the pretemporal transclinoidal approach increased the exposed length of the proximal ICA from 3.3 to 11.7 mm (P = .0001) and its circumference from 5.1 to 7.8 mm (P = .0003), allowing a 210° view of the ICA (vs 137.9°). The deep and superficial working areas also significantly widened from 53.7 to 92.4 mm2 (P = .0048) and 252.8 to 418.2 mm2 (P = .0001), respectively; the depth of the exposure was equivalent. The frontotemporal and spheno-Sylvian angles increased by 17° (P = .0006) and 10° (P = .0037), respectively. CONCLUSION The pretemporal approach can be useful for complex Pcom aneurysms by providing easier proximal control, wider working space, improved aneurysm visualization, and more versatile clipping angles. Enhanced exposure results in a potentially higher rate of complete aneurysm obliteration and complication avoidance.

The ECOG‐ACRIN Cancer Research Group trial E2511 recently demonstrated a potential benefit for the addition of veliparib to cisplatin‐etoposide (CE) in patients with extensive stage small cell lung cancer (ES‐SCLC) in a phase II randomized controlled trial. Secondary trial endpoints included comparison of the incidence and severity of neurotoxicity, hypothesized to be lower in the veliparib arm, and tolerability of the addition of veliparib to CE. Physician‐rated and patient‐reported neurotoxicity was also compared.

ABSTRACT Introduction Eczema, allergic rhinitis, and asthma are traditionally considered atopic (or allergic) diseases. They are complex, multifactorial, and are caused by a variety of different mechanisms, which result in multiple heterogeneous clinical phenotypes. Atopic march is usually interpreted as the sequential development of symptoms from eczema in infancy, to asthma, and then allergic rhinitis. Areas covered: The authors reviewed the evidence on the multimorbidity of eczema, asthma, and rhinitis, and the implication of results of data-driven analyses on the concept framework of atopic march. A literature search was conducted in the PubMed and Web of Science for peer-reviewed articles published until July 2020. Application of Bayesian machine learning framework to rich phenotypic data from birth cohorts demonstrated that the postulated linear progression of symptoms (atopic march) does not capture the heterogeneity of allergic phenotypes. Expert opinion: Eczema, wheeze, and rhinitis co-exist more often than would be expected by chance, but their relationship can be best understood in a multimorbidity framework, rather than through atopic march sequence. The observation of their co-occurrence does not imply any specific relationship between them, and certainly not a progressive or causal one. It is unlikely that a sngle mechanism such as allergic sensitization underpins different multimorbidity manifestations.

Widely used additive manufacturing technique for plastic materials is Fused Deposition Modelling (FDM). The FDM technology has gained interest in industry for a wide range of applications, especially today when large number of different materials on the market are available. There are many different manufacturers for the same FDM material where the difference in price goes up to 50%. This experimental study investigates possible difference in strength of the 3D printed PLA material of five different manufacturers. All specimens are 3D printed on Ultimaker S5 printer with the same printing parameters, and they are all the same colour.

This paper defines project success factors and aspects that are significant for successful project performance and outcomes, in the context of Information Technology (IT) projects. The list of total 38 factors is obtained through the qualitative content analysis of data collected via survey of 108 IT professionals, through one open-ended question. Detected factors are grouped into five categories: project team, project customer, project planning, project execution and project control. The results extend and support findings of the former quantitative study and the resulting project success model. They emphasize the significance of project team and project control activities for successful project outcomes.